atonal music: can uncertainty lead to pleasure?

fnins-12-00979 December 28, 2018 Time: 17:59 # 1

HYPOTHESIS AND THEORYpublished: 08 January 2019

doi: 10.3389/fnins.2018.00979

Edited by:Eckart Altenmüller,

Hanover University of Music,Drama and Media, Germany

Reviewed by:Fredrik Ullén,

Karolinska Institutet (KI), SwedenHauke Egermann,

University of York, United Kingdom

*Correspondence:Iris Mencke

[email protected]

Specialty section:This article was submitted to

Auditory Cognitive Neuroscience,a section of the journal

Frontiers in Neuroscience

Received: 14 June 2018Accepted: 07 December 2018

Published: 08 January 2019

Citation:Mencke I, Omigie D,

Wald-Fuhrmann M and Brattico E(2019) Atonal Music: Can Uncertainty

Lead to Pleasure?Front. Neurosci. 12:979.

doi: 10.3389/fnins.2018.00979

Atonal Music: Can Uncertainty Leadto Pleasure?Iris Mencke1,2* , Diana Omigie1,3, Melanie Wald-Fuhrmann1 and Elvira Brattico2

1 Department of Music, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany, 2 Center for Music in the Brain,Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus, Denmark, 3 Departmentof Psychology, Goldsmiths, University of London, London, United Kingdom

In recent years, the field of neuroaesthetics has gained considerable attention with musicbeing a favored object of study. The majority of studies concerning music have, however,focused on the experience of Western tonal music (TM), which is characterized by tonalhierarchical organization, a high degree of consonance, and a tendency to provide thelistener with a tonic as a reference point during the listening experience. We argue thata narrow focus on Western TM may have led to a one-sided view regarding the qualitiesof the aesthetic experience of music since Western art music from the 20th and 21stcentury like atonal music (AM) – while lacking a tonal hierarchical structure, and whilebeing highly dissonant and hard to predict – is nevertheless enjoyed by a group of avidlisteners. We propose a research focus that investigates, in particular, the experience ofAM as a novel and compelling way with which to enhance our understanding of boththe aesthetic appreciation of music and the role of predictive models in the contextof musical pleasure. We use music theoretical analysis and music information retrievalmethods to demonstrate how AM presents the listener with a highly uncertain auditoryenvironment. Specifically, an analysis of a corpus of 100 musical segments is used toillustrate how tonal classical music and AM differ quantitatively in terms of both key andpulse clarity values. We then examine person related, extrinsic and intrinsic factors, thatpoint to potential mechanisms underlying the appreciation and pleasure derived fromAM. We argue that personality traits like “openness to experience,” the framing of AMas art, and the mere exposure effect are key components of such mechanisms. Wefurther argue that neural correlates of uncertainty estimation could represent a centralmechanism for engaging with AM and that such contexts engender a comparativelyweak predictive model in the listener. Finally we argue that in such uncertain contexts,correct predictions may be more subjectively rewarding than prediction errors since theysignal to the individual that their predictive model is improving.

Keywords: atonal music, pleasure, uncertainty, predictive coding, aesthetic experience

INTRODUCTION

Music listening has the potential to evoke strong and intense experiences in listeners, and music-making is an activity that is present across all cultures. Music is, however, highly diverse such thatthe treatment of its features, like the spatial and temporal organization of pitches (i.e., tonality andrhythm), timbre, and form, diverge dramatically not only across but also within any given culture

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Mencke et al. Pleasure of Atonal Music

and epoch. Despite this immense heterogeneity, however, anever-increasing number of scientists feel compelled to investigatethe cognitive, affective, and neural correlates of music listening(Juslin and Sloboda, 2010; Koelsch, 2012).

While this research is still in its infancy, two trends canbe identified and serve as a motivation for the current thesis:namely that investigating specifically Western art music fromthe 20th and 21st century1 can make a key contribution tothe field. The first trend is a general tendency for musicneuroscience and music psychology (Levitin and Tirovolas, 2009)to use frameworks that either emphasize the role of emotionsin music processing (Koelsch et al., 2006; Juslin and Sloboda,2010) or that take a more cognitive perspective with a focuson similarities between music and language (Patel, 2003; Levitinand Tirovolas, 2009). In other words, there is an observabletendency for previous work to avoid engaging with musicas an aesthetic object. As previous dominant frameworks arededicated to investigating basic brain principles, they rarelyseek to illuminate the hedonic and evaluative processes thatare unique to a genuinely aesthetic experience of music, anestablished concept in the field of neuroaesthetics and musicpsychology (Juslin and Sloboda, 2010; Nusbaum and Silvia,2011; Brattico et al., 2013; Brattico P. et al., 2017; Jacobsenand Beudt, 2017). In a nutshell, by applying ‘aesthetic’ tothe music listening experience, we seek to emphasize theaccompanying mechanisms and factors that are specific to an artcontext.

A second observable trend is the tendency for existingpsychological models of aesthetic experience of music (Konecni,2005; Hargreaves, 2012; Juslin, 2013) to concentrate on music ofthe Western tonal system (Eerola, 2012; Koelsch, 2012; Janata,2013; Zivic et al., 2013; Bharucha, 2014) thereby capturing onlya very limited scope of musical genres and phenomena. Asneuroscience research has mainly used stimuli from instrumentalclassical music, recent proposals by Brattico and colleagues pointto the necessity of including in the discussion a greater varietyof genres and styles in order to provide a more comprehensiveaccount of the musical aesthetic experience (Brattico et al.,2013).

Taken together then, notably excluded from current empiricalefforts and theoretical accounts are considerations of bothmusic from non-Western cultures and the musical styles that

1The types of music that are in the center of this article are generally referred towith various terms – in scholarship as well as in music criticism and the publicdiscourse about music: Modernism, Twelve-Tone Music, Serialism, Neue Musik,avant-garde music, atonal music, or contemporary music in the Western tradition(Taruskin, 2005). Broadly speaking, they comprise Western art music of the 20thand early 21st century. Although atonality does not apply to all styles of 20th/21stcentury music, it can be seen as one defining element of the avant-garde strand ofmodernist music that evolved in Vienna around 1911 and has influenced avant-garde music before 1945 and continues to influence avant-garde music up untiltoday. In this article we therefore mainly speak of three types of music: (1) 20th/21stcentury art music (Clarke, 2017) is being used to refer to the entire phenomenonof 20th and 21st Western art music. In contrast, (2) the term ‘atonal music’ occursin a more specific context, particularly in chapter 4 and 5, where the property ofatonality (music that lacks a tonal centre; Forte, 1977; Forte, 1998) is a crucial aspectin our argumentation. Finally, (3) the term ‘contemporary classical music’ (in theWestern tradition) is being used, when there is a clear emphasis on recent artisticpractice.

are addressed in this paper as aesthetic objects requiringcomprehensive and systematic investigation. Thus, while anincreasing number of studies have begun to clearly acknowledgethat music is heterogeneous and that an enormous variety ofeffects may emerge from the diversity of musical genres andstyles (Krumhansl et al., 2000; Brattico et al., 2011; Salimpooret al., 2011; Alluri et al., 2012, 2015, 2017; Zatorre andSalimpoor, 2013; Dean and Bailes, 2016; Dean and Pearce,2016; Poikonen et al., 2016), there is still a need to stress theimportance of an approach that treats music purely as art,by encompassing arguably the most innovative, experimentaland challenging of musical styles, namely 20th/21st century artmusic.

Exactly for the reasons stated above we propose that20th/21st century art music represents a promising topic forfuture research into music listening as an aesthetic experience2.Through atonal music (AM), the set of styles or musical“languages” that have been investigated until now can beexpanded on. Research on AM has the potential to revealneural mechanisms that might be unique to the aestheticexperience of music, as opposed to those focused on rule-basedprocessing of structure in Western tonal music (TM). Giventhat it is, at first glance, a potentially unpleasant environment,we argue that AM offers one especially promising tool withwhich to discover how experiences that might be avoided ineveryday situations can be appreciated in an aesthetic context:a mystery that leads right into the notion of the transformativepower of art which is so prominent in modern Westernthinking.

In the following sections, we first historically contextualize20th/21st century art music, before reviewing the psychologicalresearch that thus far directly or indirectly addresses AM. Thesubsequent section describes, by means of music theoreticalanalyses and music information retrieval methods, how a specifickind of AM (that we expand on in the next sections) differsfrom Western TM. We then examine a variety of factors andmechanisms that may influence the enjoyment of AM. Wegroup these into three categories namely person related factors,extrinsically driven factors and intrinsically driven factors. Theterms extrinsic and intrinsic are used to denote the originof information that influences the kind of pleasure derivedfrom the music. Specifically, with extrinsic factors we refer tomechanisms that rely on information external to the stimulus.In contrast, mechanisms subsumed under intrinsic factors relyon information processing that is stimulus internal. Followinga synthesis of mechanisms we conclude with future prospects

2Not only do musical styles and strands not possess a clear definition as addressedin footnote 1, but equally the terms tonality and atonality have no sharp definition.For the purpose of this paper, however, the following explanation tries to clarifyour usage of these terms: When we address tonal music we mainly refer to musicalstyles of the Western culture from the 17th to the early 20th century (and in somemusical genres, specifically genres of popular music, even until today). Due to theusage of a tonal scale, most of the music has a tonal centre (the tonic) and thushierarchical relations between pitches and chords. This can be seen as one of therare but essential characteristics that is linked to the concept of tonality (Dahlhaus,1967; Hyer, 2001). Complementarily, when writing about atonal music, we arereferring to those kinds of 20th/21st century art music that are characterized bythe lack of a tonal scale, tonal centre and a regular meter.







regarding the role we suggest 20th/21st century art music shouldplay in future research in the field of neuroscience of music.

WESTERN ART MUSIC FROM THE 20thAND 21st CENTURY

A Brief HistoryThe origins of 20th/21st century art music can be dated as farback as the late 19th century when compositional innovationswere characterized by a gradual dissolution of the Western tonalsystem, which in turn had been based mainly on the major-minor tonality developed in the 17th century (Dahlhaus, 1967,1990). However, the clearest steps toward consolidating the fieldof 20th/21st century art music were made by Arnold Schoenberg(1874–1952), who gradually moved away from using tonalityand traditional keys in his compositional works and in finallyproclaiming the “emancipation of dissonance” (Dahlhaus, 1978).He may be considered one of the leading figures of the avant-garde of the 20th century. While in the early phase, Schoenberg’scompositions followed so called “free atonality,” he later inventedthe more structured twelve-tone technique around 1920, whichaimed at treating all twelve tones within an octave as equivalentin a musical piece (Dibelius, 1988). This resulted in music thatwas strongly dissonant and which lacked a tonal hierarchicalstructure – a characteristic that composers aimed for. An evensterner approach was then developed by the serialists of the 1950s(central figures being O. Messiaen, P. Boulez, K. Stockhausen,L. Nono - also protagonists of the Darmstadt School), whonot only pre-determined the use of pitches as Schoenberg did,but also transferred this method of equal treatment to otherparameters like dynamics and duration of tones, resulting in astrict “parametric thinking” (Grant, 2001, p. 62).

While Schoenberg and the Serialists founded compositional“schools” in the first half of the 20th century, the latterhalf witnessed an ever increasing individuality and pluralismof musical styles and forms (Hüppe, 2016). Furthermore,compositional innovation emerged on several levels other thanmelody and harmony, including rhythm, timbre, and form. Withregard to innovations in form, for instance, very short pieceslike Stockhausen’s early piano pieces (Piano Piece III. lasts 40 s)were composed alongside pieces like Organ2/ASLSP by John Cage(composed to last a few centuries). With regard to innovations intimbre, the musicality of noises (musique concrète) was exploredalongside the noisiness of classical instruments. By inventingmusique concréte instrumentale, Helmut Lachenmann (∗1935)radically re-thought how instruments could be played (e.g., usingthe back of the bow on string instruments). The use of electronicmeans to produce tones expanded this field even more (e.g.,Stockhausen in the 1960s).

These examples of powerful artistic innovations and theoccurrence of new aesthetic paradigms in each decade are typicalof Western art music from the 20th and 21st century. Thisinnovative strength can be seen as its teleological drive whichwas most influentially formulated by Th. W. Adorno in hisPhilosophy of New Music (Adorno, 1949). This drive originatedfrom the aesthetic thinking of Schoenberg who claimed that

music composition must overcome academic norms and oldcategories and that the composer similarly must be stronglycritical not only toward the previous but also toward the popularand the masses (Utz, 2016). While these transformative processesare typical for paradigm shifts, in the context of ‘New Music’(as termed by Adorno), it became an ongoing aesthetic premise(Hüppe, 2016). Creating music that overcomes old listeninghabits by means of presenting something entirely new was acentral motivation for artists. However, for the listener this meantbeing presented with a vast diversity of musical vocabulary thatrepresents an enormous challenge for the listening process.

Contemporary Classical Music – AnArtistic Niche?Nowadays, in keeping with the above-described aestheticpremise, a great deal of instrumental contemporary classicalmusic is based on the free treatment of tonality, meter and form.However, while composers utilize independent and often self-designed systems in order to create regularity as a base for theircompositions, this does not always facilitate the listening process.An additional challenge faced by the listener is identifyingclear musical genres, which would serve as a stylistic referencepoint and which would provide information for pre-classification(Leder et al., 2004). Certainly, the demanding nature of thismusic has contributed to the status of contemporary classicalmusic as a niche in modern cultural life (Ball, 2011). However,the last 30 years have seen a huge growth in the number ofmusic ensembles specializing in the music of the 20th and21st century (Fricke, 2011). Even though this musical styledoes not attract audiences as large as pop or classical musicconcerts, systematically including music from the 20th and21st century is high on the agenda of most large concerthouses where “hybrid concepts,” i.e., programs consisting ofboth classical and contemporary classical music (Maletz, 2011,Chapter 2), often receive significant funding (Fricke, 2011,p. 174). Moreover, there are several ensembles in Europe whichfocus solely on 20th/21st century art music, like the EnsembleModern (Germany), Ensemble Intercontemporain (France),Asko/Schönberg (Netherlands), Klangforum Wien (Austria), andLondon Sinfonietta (United Kingdom), to name but a few.Therefore, although this style of music appears to be a nicheand is less popular than other musical styles, it representsan impressively rich and highly diverse musical phenomenon(Hüppe, 2016). Contemporary classical music nowadays is ahighly interactive artistic area frequently linked to other artsectors, like fine arts, installation and performance art (Bishop,2005). Thus, contemporary classical music can be seen as oneof the driving forces of the avant-garde and contemporary artcreation.

Aesthetic Values of 20th/21st Century ArtMusicAs indicated in the previous sections, creating and presentingsomething truly novel plays a crucial role in most 20th/21stcentury art music. This musical newness can partially be seenas a critical factor for the mere listening experience but also







for the appreciation of it. Similarly, this music’s aim, namely tosensitize the listeners’ hearing/audition and to literally overcomeperceptual habits, can inform our understanding of the positiveaesthetic values, the underlying appreciation and the “sensoryattractiveness” (Utz, 2016) that accompanies a positive aestheticexperience of AM.

In the context of journalistic writings about contemporaryclassical music, the music is often described as a music type thatpresents something new. Moreover, pieces from the 20th/21stcentury often require excellent and highly developed playingskills, which can induce fascination and therefore a positivevaluation by the audience3. Finally, the musicians’ motivationfor playing contemporary classical music is linked to freeexperimentation with and exploration of new sounds, the closecollaboration with composers and importantly the possibility topresent something that has never been heard before (Meißnerand Morawek, 2012). Curiosity, energy, and openness (Meißnerand Morawek, 2012) are often reported as being necessary forplaying – and also enjoying – contemporary classical music.Thus, novelty, innovation, and the challenge it affords mayall be conceptualized as positively valued and may guide theappreciation of contemporary classical music.

STATE OF RESEARCH – ATONAL MUSICIN THE EMPIRICAL MUSIC LITERATURE

Decades of psychological research have shown that a tonalmusical context has a strong psychological representation,whereas musical events that do not occur in a tonal context areless stably represented (Krumhansl, 1979). For instance, whilelisteners are able to pick correct reductions of tonal excerpts intonal contexts, they are not able to do so with atonal excerpts(Dibben, 1994). Atonal music does not provide a tonic referencepoint. Nor does it provide corresponding chords of significantbut lower importance. This lack of pitch hierarchy not onlyhas perceptual but also cognitive implications. Indeed, Schulzeet al. (2012), when testing memory for tonal and atonal melodieswith a paradigm in which the sound sequences had to bekept in memory, found that both musicians and non-musiciansperformed significantly better in the tonal sequences. The authorsreasoned that structured material present in the tonal task enablesbetter working memory performance (Lerdahl and Jackendoff,1983). In adults, it has been shown that it is more difficult torecognize transposition in the context of novel atonal relative totonal melodies (Cuddy et al., 1981; Dowling et al., 1995).

The notion that the brain tracks hierarchical structures (e.g.,Nelson et al., 2017) and that such hierarchical structures facilitatethe storing and recalling of information has been evidenced inmany different domains (e.g., Mandler, 1984). Specifically, in thedomain of music processing it has been shown that the tonic,the subdominant and the dominant strongly influence processingspeed (Tillmann et al., 2008). These three chords of music are

3See reviews in newspapers and journals that report about contemporaryclassical music. German-speaking countries for instance: neue musik zeitung,Neue Zeitschrift für Musik, Positionen, Musiktexte – Zeitschrift für Neue Musik.English-speaking area: Tempo, Musical Times.

known as the “harmonic core” and represent the pillars of tonalhierarchical organization in Western TM from the 17th centuryonward (Dahlhaus, 1967; Bharucha and Krumhansl, 1983). Thishierarchy is inherently linked to the tonal scale, in which everytone within an octave has a specific function. The first tone of thescale, called the tonic is the “head of the hierarchy” (Krumhansland Cuddy, 2010) and represents the auditory and cognitivereference point (Rosch, 1975). All other functions follow thisreference in a hierarchical manner (within one key). Even thoughthis “pitch centrality” can be found across musical styles andcultures (Krumhansl and Cuddy, 2010), AM might represent anexceptional case due to its equal treatment of all tones within anoctave and the resulting lack of a clear tonal center.

An interesting insight into how the brain deals with atonalexcerpts may be drawn from examining the strategy listenersuse to remember the excerpts (Mikumo, 1992). In one task,musically trained subjects were required to compare melodiesthat had been interrupted by different “retention intervals” and toreport on their encoding strategies. Results revealed that for tonalmelodies, participants used a verbal (e.g., note naming) strategywhereas for the atonal contexts they used “rehearsal strategies(such as humming and whistling)” (Mikumo, 1992). In additionto storage, another interesting insight may come from how atonalsequences are processed online in comparison with tonal ones.It has been shown, through research on auditory scene analysis(Bregman, 1990) and grouping mechanisms in music perception(Deutsch, 1999), that grouping in music facilitates recognition,processing and recall of melodies or chunks of items (Creel et al.,2004). Importantly, such structures are held to be key to theformation of predictions (van de Cruys and Wagemans, 2011).

There is only very little research on how predictions aregenerated in atonal musical contexts, but it has been suggestedthat listeners generate expectations in a contrary manner.Specifically, Ockelford and Sergeant (2012) showed – basedon probe-tone experiments with atonal phrases – that listenersexpect the “opposite” to happen, in other words applying an“antistructure“ toward future musical events. In Ockelford’sexperiments (2012), it was found that after listening to a 12-tone series, recipients expected that none of the already-heardsounds would be repeated, and that none of the already-heardsounds would suggest the existence of a key. These findings arein line with those from Krumhansl et al. (1987), suggesting thatlisteners use tonal schemas when listening to AM. Generally,in comparison to tonal major/minor contexts, atonal contextsevoke weaker expectancies (Vuvan et al., 2014) although theorganization of AM seems to be recognizable by listeners (Clarkeand Krumhansl, 2017), and particularly the segmentation ofsound is based on similarity and/or contrast (Deliège, 1989).Research from cross-cultural studies suggest that familiarity andenculturation with a particular musical style shapes expectancyprocesses (Curtis and Bharucha, 2009; for a review on cross-cultural music perception and cognition see: Stevens, 2012).This suggests that familiarity with AM might equally modulateexpectancy mechanisms in the listening process.

With regard to how the high degree of dissonance(simultaneous and subsequent) in AM is received, researchon processing of and preference for consonance vs. dissonance







cannot be ignored, particularly when considering predictionmechanisms in music. Research suggests that the preferencefor consonance chords stems from its harmonicity. Consonantdyads have many frequency components in common (Dewittand Crowder, 1987; Tramo et al., 2006; Ebeling, 2008). Moreover,their partials contain smaller integer ratios than those ofdissonant dyads. In dissonant intervals, the harmonics aredissimilar and the frequency components lie in the criticalbandwidth of the cochlea (Greenwood, 1991). For the listener,this leads to the perception of auditory roughness (Plompand Levelt, 1965) which underlies sensory unpleasantness.Another hint at the preference for simpler frequency ratiosis the finding that most of the scales used for making musicconsist of 5–7 tone scales which correspond to the harmonicseries (Gill and Purves, 2009). However, in contrast to whatis suggested by the dichotomy ‘consonance vs. dissonance,’ noclear line can be drawn between them as there is an acousticalas well as perceptual continuum between the simplest and mostcomplicated frequency ratios.

Preference for more consonant sounds nevertheless seems tostart at very early developmental stages with newborns optingto listen to consonant rather than dissonant intervals (Zentnerand Kagan, 1998; Trainor et al., 2002) and a link has been madebetween the less robust neural responses to dissonant relative toconsonant chords that are found at the brainstem level (Bidelmanand Krishnan, 2009). To which extent and in which way thesepreferences are shaped by learning and culture is a long-debatedissue, a detailed examination of which is outside the scope of thispaper. However, we discuss the role of learning in the context ofmere exposure effects in following sections.

While behavioral research on perceptual and cognitiveprocessing of atonal melodies has been minimal, even lessresearch on AM has been carried out in the neuroscientificdomain. Indeed, very striking is that when AM is used in suchstudies, it serves only rarely as a style of music in its own rightbut as examples of unpleasant dissonant music (Blood et al.,1999; Gagnon and Peretz, 2000; Flores-Gutiérrez et al., 2007),as a less preferred musical genre (Cross et al., 1983; Smithand Melara, 1990), as stimuli to induce “‘fearsome’ emotions”(Flores-Gutiérrez et al., 2007) or as a control stimulus for “non-musical auditory inputs” (Trost et al., 2012, p. 2770). However,a few studies have indeed examined AM as a musical style inits own right. One particularly relevant approach has taken aninformation theoretical perspective to atonal and serial music andhas shown the entropy of such melodic excerpts to be high andthe information content of individual events to be low (Deanand Pearce, 2016). Indeed, due to the lack of a tonal centerconstituting an auditory reference point, individuals listening toAM may be expected to experience high “predictive uncertainty”(Hansen and Pearce, 2014). Similar characterizations can betraced back to the music philosopher Leonhard Meyer, the firstto apply information theoretic notions to theories in musiccognition when he proposed that a musical style is cognitivelyrepresented as a network of probabilities in the listener’s brain(“internalized probability systems,” Meyer, 1956, p. 414). Aninformation theoretical perspective (Pearce, 2005; Huron, 2006)will later serve as a foundation to lay down our hypotheses

regarding how pleasure might be derived from an encounterwith AM.

HOW IS ATONAL MUSIC DIFFERENT?SINGLE PIECE AND CORPUS ANALYSIS

The following part of this paper moves on to compare tonal withAM from different methodological angles, in order to illustrateconcretely the potentials of AM. While we use music theoreticalanalyses to give a functional analysis as well as to provide a visualillustration of the scores of the musical excerpts, the analysesconducted with the MIR (music information retrieval) toolboxfor Matlab (Lartillot and Toiviainen, 2007b) have the purposeto calculate particular features both in single pieces and in acorpus analysis. These results shall demonstrate characteristicsof AM from a computational perspective. The focus here is ontonality and meter rather than on timbre and form as thesefeatures represent the most salient characteristics of a musicalpiece in terms of its texture. While tonality refers to the verticaland horizontal relationships between the pitches (i.e., melodyand harmony), meter relates to the temporal organization of thepitches. In contrast, timbre represents the specific character andquality of sounds, while form refers more to the overall structureof a musical piece.

Single Piece Analysis by Means of MusicTheoryIn order to represent AM, we chose pieces that do not followthe rules of traditional harmony and therefore lack a clear tonalcenter as well as a metrical regularity. However, to not restrict theselection to a single musical epoch, we consciously chose piecesthat belong to different sub periods of 20th/21st century music(see Table 1). For the Western tonal styles, we selected piecesfrom the so-called common practice period (encompassing thebaroque, classic and romantic epoch but often subsumed simplyunder the term classical music: 1600–1910; Hyer, 2001), showinga clear tonal center and an equally strong metric regularity.Table 1 shows the selected pieces including composer, date ofcomposition, corresponding sub-period and period.

Beginning with meter, the scores (Figure 1) show that themetrical regularity in the classical pieces (Figures 1A–C) stemsfirst and foremost from the strictly followed time signature.By contrast, most of the atonal pieces (Figures 1D–F) lack aregular pulse. Moreover, in Widmann’s Fleurs du mal, the timesignature changes in each bar which results in an irregularstress pattern. Another sign of the irregular meter in theatonal pieces are the highly diverse note lengths. For example,in Stockhausen’s piano piece, a wide range of note lengthsappears, beginning from a half-note as the first note untilsixty-fourth notes with additional triplets and sextuplets. Incontrast, the classical pieces use less diverse note lengths, asin the piece by Bach, where mainly sixteenth notes run over afundament of crotchets. Further, in the pieces by Mozart andChopin, the metrical regularity derives heavily from the bassaccompaniment with its constant pulse of quavers (Mozart) orcrotchets (Chopin). Finally, while in the classical pieces the lower







TABLE 1 | Overview of selection of pieces and periods.

Composer Piece Date of composition Subperiod Period MIR excerpt in seconds

J. S. Bach Goldberg Variations, Variatio 12 1741 Baroque Classical music 0–30

W. A. Mozart Piano Sonata No. 8, K. 310 1778 Classic Classical music 0–30

F. Chopin Waltz, A-Minor 1843 Romantic Classical music 0–30

K. Stockhausen Piano Piece No. VIII 1954 Serialism 20th/21st century music 0–30

W. Rihm Piano Piece No. 5 1975 New Simplicity 20th/21st century music 0–30

J. Widmann Fleurs du Mal 1996/97 Contemporary 20th/21st century music 49–79

The selection of pieces had two aims: (1) Classical pieces had to be strongly tonal and highly regular in meter. Pieces from the 20th/21st century had to be highly atonaland similarly highly unregular in meter (we refer to them as “atonal pieces”). (2) We then aimed at covering a scope of musical styles as broad as possible. Therefore wetook one piece of each of the main subperiods from the classical (1700–1850) and the 20th/21st century period (1910–2000). Moreover, it is indicated which parts ofthe pieces were chosen for analysis: “MIR excerpts in seconds” refers to the excerpt we chose for automatic feature extraction by MIR toolbox for Matlab (Lartillot andToiviainen, 2007a; see Figure 2). For this we took the first 30 s of the audio recordings, except for the Widmann piece, as it starts with a 50-s long repetition of a singlenote (accordingly the score excerpt of the Widmann piece displays exactly this part).

and upper voice share the same meter, in the atonal ones, bestseen in the Stockhausen piece, both voices are treated completelyseparately.

With regard to tonality, the scores demonstrate key differencesbetween the classical and atonal pieces. In the classical pieces,the home key is set up right in the first bar. Moreover, adifferent function within the key appears within the followingbars (for a functional analysis see Figure 1). This cadencestructure is one of the most important features of classical andWestern tonalities and is closely correlated with the structuralunfolding of the melodic subjects and the piece as a whole.Since the cadence is based on sequences of triads and tetrads,those pieces are additionally characterized by a high degree ofsensory consonance. In contrast, the metrically less structuredatonal pieces are accompanied by a different treatment of tonalitywhereby cadence-like chords are lacking. Indeed, rather thanhaving a symmetric intervallic structure, a highly diverse range ofdissonant intervals occur, leading to a high degree of intervallicentropy (for a more detailed analysis see Figure 1, caption).

Finally, a very striking difference between the classical andatonal pieces is their Gestalt quality, again observable fromthe score. The main Gestalt laws are proximity and similarity(van de Cruys and Wagemans, 2011), and Gestalt theory playsa key role in the current understanding of music perception(Bregman, 1990; Reybrouck, 1997; Deutsch, 2013). Whereas theclassical pieces provide ascending and descending scales and thusavoid large jumps between notes, the atonal pieces, in particularRihm’s piano piece (Figure 1D), are characterized by large jumpsand frequent changes of melodic direction thus impeding easygroupings based on Gestalt principles4. Moreover, both withregard to the note lengths mentioned previously and the melodylines, it is very clear that the classical pieces tend to show a highself-similarity in comparison with the atonal pieces (Narmour,1990). This is in line with results of a corpus analysis whichshowed evidence that classical music is characterized by smallpitch tendencies (Huron, 2001).

4However, it is worth noting that the AM pieces, particularly Figures 1E,F, displaysome kind of musical gestures. Even though the groups of notes are not made up bytonal hierarchies, there is still a formation happening that could be called a musicalgesture (Godøy and Leman, 2010).

Single Piece Analysis by Means ofAutomated Extraction of FeaturesFor the purpose of describing music with quantitative andautomated methods we used the MIR toolbox for Matlab(Lartillot and Toiviainen, 2007b), which has been used in recentneuroscientific studies on music (Brattico et al., 2011; Brattico P.et al., 2017; Alluri et al., 2012, 2017; Bogert et al., 2016; Burunatet al., 2016; Poikonen et al., 2016). The tool allows the tracking ofcontinuous developments of changes in both low-level features,including spectral composition, roughness and timbre, as well ashigh-level features, such as tempo, pulse clarity and key clarityvalues. The MIR approach is useful for the investigation of therelationship between perception, brain responses and musicalstructure since the dynamic nature of musical stimuli requires aclear quantification of features over time. We chose two featuresfrom the MIR toolbox to describe tonality and metrical regularityof musical pieces, using the functions key clarity and pulse clarity.

Pulse clarity function estimates the rhythmic clarity indicatingthe strength of the beats estimated based on interonset intervals.An autocorrelation function used in a constrained time-windowassesses the self-similarity and thus gives the degree of pulseclarity. High pulse clarity indicates a regular and low pulseclarity an unregular metrical structure. The key clarity functionis calculated based on pitch chromagram and the Krumhansl–Kessler algorithm of matching pitch class profiles to key profiles(Krumhansl, 1990; Toiviainen and Krumhansl, 2003). Both keyand pulse clarity measures can provide a quantitative value ofthe perceptual experience by the listener. In fact, both havebeen shown to be highly correlated with perceptual ratings ofindividuals (Alluri et al., 2012). In the current analyses of the sixpieces, we used a window size of 5 s and a hop factor of 33% forboth key and pulse clarity (Recordings used see SupplementaryTable 1).

Key clarity shows how strongly a key is represented at anygiven moment. We would thus expect higher values for classicalpieces due to their tonal constitution and lower values for theatonal pieces since they do not convey a key as clear as theirtonal counterpart. Indeed, Figure 2A and Table 2 demonstratethis to be the case. However, it may be observed that the pieceby Bach achieves a rather low key clarity value compared to theother two classical pieces. This can be explained by the fact that







FIGURE 1 | Single piece analysis by means of music theory. Metrical regularity in classical pieces is readable in the strictly kept time signature at the beginning, theequal lengths of tones and the repetitive accompaniment in (B,C). In contrast, atonal pieces (D–F) show different time signatures and a large variety of note lengths,resulting in a complex rhythm without a regular meter. Tonality in the classical pieces is indicated by the set-up of the home key in the beginning. In (A) the key of Gmajor is prevalent in the first bar, being presented in a scale, supported by the G in the bassline. In the next bar the key switches to the dominant, D major,supported by the third (F#) in the bassline. In (B,C), which reveals a homophonic texture (in contrast to a polyphonic texture in A), the clear key structure is conveyedmainly by the chordal accompaniment, where each chord belongs to a particular function within the home key. The piece in (C) starts with the tonic (‘t’, A minor, 1stbar), followed by the subdominant (‘s’, D minor, 2nd bar), followed by the parallel of the dominant (dP) G major with a seventh, which leads to C major, the majorparallel key of A minor (‘tP’). This tonal structure is then repeated. In contrast, the atonal pieces reveal a completely different texture, leading to a more flexiblestructure that is first of all lacking triad-building chords. However, the chords that appear are highly dissonant. This becomes clear with looking at intervals. The firstinterval of (D) displays a G#, A and a F# in parallel leading to a highly dissonant sound especially due to the second. The third sound of (E) consists of six notes inparallel, similarly dissonant, again partly due to a minor second between E and F. Moreover, also consequent intervals suggest a dissonant, thus atonal structure.For instance in (F) the second bar consists of a ninth, 2 minor sevenths, 1 major seventh and a tritone, thus covering most dissonant intervals.

this contrapuntal piece consists of different, independent voices,intertwined with each other (as compared to homophony whereone main voice is accompanied by a mainly chordal baseline, as isthe case in Figures 1B,C). Due to the specific implementation ofkey clarity in the MIR Toolbox (see description above) a swiftlymoving diatonic melody (in the given 5 s window) as can befound in the Bach piece will result in more saturated pitch class

profiles and hence reduced key clarity. More specifically, thepassing tones (on diatonic scale degrees 2, 4, 6, and 7) will bemore emphasized compared to the triad tones (scale degrees 1,3, 5), thus the clarity of the key profile decreases. The algorithmis not able to differentiate between low-density chromaticism andhigh-density diatonic movement of counterpoint. This can alsobe seen in the higher key clarity of the Stockhausen compared to







FIGURE 2 | Single piece analysis of features (key and pulse clarity) by means of automated extraction. This figure displays the results of the analysis of single pieceswith MIR Toolbox for Matlab (Lartillot and Toiviainen, 2007a). Red lines represent classical pieces, blue lines atonal pieces. In (A) results for key clarity are displayed,using a window size of 5 s and a hop factor of 33%. Even though at some points the values of modern pieces lie above classical piece values, classical pieces tendto obtain higher values over time (compare Table 2). In order to demonstrate the scope of key clarity that the algorithm is able to reveal we included a C Major chord(yellow) and white noise (green) into our analysis as reference points. Even though the representation particularly of the white noise indicating peaks and lowsextracted from a constant signal can be seen as critical, it gives a reference of how to evaluate the key-related feature extraction of the pieces (see Table 2 forabsolute and mean values of key clarity). In case of pulse clarity (B), classical pieces obtain much higher values than atonal pieces and results are more significant.However, the Chopin piece shows lower values, which can be explained by the rubato interpretation of the musician, which generates slight changes in tempo atformal borders within a piece (e.g., “ritardandos” = deceleration). Particularly, at around 10 s, a drop of pulse clarity can be seen which is reflecting the decelerationof the pianist at the end of the first presentation of the main motif.

the other atonal pieces due to its looser texture and lower eventdensity. We included a C Major chord and white noise into ouranalysis in order to demonstrate the limits of this algorithm (seeFigure 2A).

Pulse clarity provides estimations of the regularity of rhythmicor metrical pulsation (Lartillot et al., 2008). In Figure 2B theclassical pieces achieve much higher values than the atonal pieces,whereas the piece by Bach obtains overall highest values, likelydue to the regular movement in sixteenth notes (see Figure 1A).To explain the low scores of the Chopin piece, we assume thatthe specific rubato interpretation by M. Takedo-Herms is thereason. “Ritardandi” are a style-adequate feature in Romanticperformance practice that consists in retardations of tempoplayed at formal borders, like phrase boundaries, which inevitablyresult in an attenuation of the clarity of the pulse.

Corpus Analysis of 100 Piano PieceExcerptsHere, due to our interest in the structural (and textural)differences between Western tonal and AM, we sought tocompare tonality and meter across the styles. We accordinglyselected types of music that we considered to represent exactlythose two features clearly.

Our corpus analysis examined 50 TM excerpts from theclassical period (TM, tonal music) and 50 AM excerpts (AM,atonal music). Here the main criterion was the comparability ofTM and AM with regard to texture (see selection of composersand pieces in Supplementary Table 2). Since the AM pieceslack a homophonic texture and are characterized by a moreindependent treatment of melodic lines, we contrasted these withpolyphonic pieces from the Baroque period (which correspondsto ‘TM’). Further, due to a high structural diversity within pieces,we took only 40–60 s segments into consideration for analysis.The segments were extracted so that tempo, event density andvolume were kept as similar as possible within and across TM andAM segments. Moreover, excerpts were extracted to align withphrase and formal boundaries. In short, the resulting segmentsprovide a collection of musical excerpts that are assumed torepresent both TM and AM in terms of two of its core stylisticfeatures, namely (a)tonality and the (ir)regularity of meter. Theresults confirmed our assumptions and demonstrate that theselected segments from TM and AM considerably differ withregards to their key and pulse clarity values (see Figure 3):Two independent-samples t-tests showed significant differencein key clarity values for atonal (M = 0.5 ± 0.1) and tonal(M = 0.8 ± 0.1) excerpts [t(98) = 15, p < 0.0001; dCohen = 3;







TABLE 2 | Key clarity values.

Musical style Mean key clarity STD Mean for musical style Event densityper second

C-Major chord 0.7987 0.0034

J. S. Bach 0.6987 0.0709 3.778

Tonal Music W. A. Mozart 0.6951 0.1217 0.7085 1.8388

F. Chopin 0.7317 0.0972 1.5714

K. Stockhausen 0.6206 0.1122 0.53494

Atonal Music W. Rihm 0.5034 0.1278 0.5667 2.4072

J. Widmann 0.5761 0.0937 0.83584

White Noise 0.5048 0.1270

Tonal pieces on average obtain higher key clarity values than atonal pieces, as the “mean for musical style” suggests. However, in order to show limitations of the algorithmwhich the toolbox computes key clarity values with, we included a C-Major-chord and white noise into the analysis, expecting highest value representation in the case ofthe chord and lowest values for the white noise. Those scores represent actual frame of values one can expect from this algorithm.

FIGURE 3 | Corpus analysis of 100 piano pieces. Figure shows the mean keyand pulse clarity values for each musical style, extracted with MIR Toolbox forMatlab (Lartillot and Toiviainen, 2007a). Two independent-samples t-testswere conducted to compare the two corpora (AM = atonal music; TM = tonalmusic). There was a significant difference in key clarity values for atonal(M = 0.5, SD = 0.1) and tonal (M = 0.8, SD = 0.1) excerpts; t(98) = 15,p < 0.0001; Effect size dCohen = 3; 95% CI (2.19/3.81). Similarly, there was asignificant difference in pulse clarity values for atonal (M = 0.2, SD = 0.1) andtonal (M = 0.4, SD = 0.1) excerpts; t(98) = 10, p < 0.0001; Effect sizedCohen = 2; 95% CI (1.32/2.68). The results represent the striven manifestationof features and demonstrate the reliability of this computational approach.

95% CI (2.19/3.81)]. Results for pulse clarity values for atonal(M = 0.2 ± 0.1) and tonal (M = 0.4 ± 0.1) excerpts [t(98) = 10,p < 0.0001; dCohen = 2; 95% CI (1.32/2.68)] were similarlysignificant.

The following part of the paper examines particular kinds ofmechanisms that could contribute to a pleasurable experiencewith AM. However, it needs to be clarified that particularlywhen it comes to theories on how AM might be cognitivelyprocessed and neurally represented, a specific style of AM isbeing addressed. The following argumentation is based on astyle of AM that does not suggest any key nor any regularityin meter (see our analyses above as well as SupplementaryTable 2 for more exemplary pieces and composers). Accordingly,musical pieces from 20th/21st century art music are excludedwhen they do not fulfill these criteria, i.e., if they entail tonalrelationships or any form metrical regularity (style example:minimal music; piece example: Atmosphères by György Ligeti).When comparing Western TM to AM, we refer to all musicthat uses the Western tonal scale and is composed due to therules set up in the period of common practice (Hyer, 2001).However, to provide a sharp contrast the reader is encouraged,when speaking of Western TM to think of music from thecommon practice period. This is due to the fact that thisclassical or art music in contrast to more popular forms ofWestern TM show a higher degree of structural complexityand density, a property that it has in common with the kindof AM we focus on and thus serves as an equal comparablestyle.

ACCOUNTING FOR MUSICALPLEASURE OF ATONAL MUSIC

Empirical or scientific approaches of the experience of pleasurewith 20th/21st century art music are sparse. As mentioned inthe discussion on its aesthetics, this style of music intended andcontinues to intend to confront the listener with unknown andnovel sounds and structures.

We now examine person-related factors as well as extrinsicand intrinsic factors that have been reported to contribute to anincrease in pleasure in the context of an aesthetic experience ofmusic. Particularly, we discuss the extent to which they couldaccount for pleasure derived from AM.







Person-Related FactorsWhen considering mechanisms that bring one to engagewith 20th/21st century art music, personality traits might beconsidered a predictive variable. With regard to music listening,individuals exhibiting high ‘openness to experience’ show agreater appreciation of complex music (Nusbaum and Silvia,2011), are more receptive to chills and absorption (Silvia andNusbaum, 2011) and are more likely to use music for cognitivestimulation (Rentfrow and Gosling, 2003). Moreover, devotedcontemporary classical music listeners might score higher in the“Need for Cognition” (Cacioppo et al., 1996), which is allocatedto the factor “openness to experience” of the NEO-PI (Mussel,2014).

In the context of explaining creativity, Perlovsky refers tothe so-called “knowledge instinct” (Perlovsky and Levine, 2012,p. 292) to describe human nature, suggesting that individuals aregeared toward seeking information and discovery. It has beensuggested that humans, as well as animals, inherently possessa drive for curiosity (Jepma et al., 2012; Levine, 2012) andthat exploratory behavior, driven by novelty, is associated withreward in the long-term (Bevins, 2001). This trait may be morepronounced in certain individuals than in others. For instance, increative people a relationship between “openness to experience,novelty-seeking, and preference for complexity” (Feist and Brady,2004, p. 79) has been shown. Moreover, in behavioral geneticsthe trait ‘openness to experience’ (Costa and Mccrae, 1992) hasbeen associated with modes of dopamine neurotransmissionthat are, in turn, associated with a “variation in reward andemotion processing” (Peciña et al., 2013, p. 878). Taken together,individuals who have a preference for AM may score high in‘openness to experience.’ They are likely better at withstandinguncertainty and listen to AM to fulfill their enhanced need forcomplexity and novelty.

Extrinsic FactorsEnjoying AM might be strongly determined by factors thatcan be subsumed under the effects of framing a situation (theterm “frame” was coined by the sociologist Goffman, 1974).“Pre-classifications” can serve as important cues in particularin modern art (Leder et al., 2004, p. 493), so that for instancean object like the famous pissoir by Marcel Duchamp canbe recognized as an art object at all. The mere fact that itis exhibited in a museum provides the relevant cue for theperception as art rather than as an utilitarian object. Mostly, suchpre-classifications operate on a socio-culturally coded contextuallevel, like a concert hall, a gallery or a particular performativesetting that takes place at a festival. Moreover, they represent thefirst processing stages in the context of an art experience (Lederet al., 2004; Pelowski et al., 2017).

Resulting changes in perception, evaluation and cognitiveprocessing might appear such that individuals expect pleasurablemoments, focus on particular stylistic or formal features or –which can be seen as the most important factor for the contextdiscussed here – adopt a higher “tolerance for surprise, disgust,or ambiguity” (Pelowski et al., 2017). In the literature onmusic, such effects have also been defined under the term

“aesthetic framing” (Juslin, 2013, p. 13) or external context(Brattico et al., 2013), mainly referring to a change in theattitude toward the music due to external factors. For instance,a concert being experienced at a famous location may lead toadditional value and meaning attribution. Another importantnotion with regard to the aesthetic attitude is that of it beingan “intentional contemplative approach to an object, which isperceived, conceptualized and evaluated as detached from itsutilitarian functions” (Reybrouck and Brattico, 2015, p. 70).

Experiencing joyful moments with AM might arise from amechanism called “cognitive mastering” (Leder et al., 2004),which in the visual domain was shown to play a major role in theenjoyment of abstract paintings. Cognitive mastering describesthe capability of humans to increase their appreciation of artusing cognitive constructs like information about the artworkor the artist. Cognitive constructs can also occur in the formof social constructs. In an fMRI study, evidence was foundthat a “cortical network typically associated with mental stateattribution” (Steinbeis and Koelsch, 2009, p. 619) was more activewhen subjects were told that the pieces they listened to werewritten by a composer compared to when they were told thesame pieces were written by a computer (Steinbeis and Koelsch,2009). This suggests that cognitive constructs can potentiallyinfluence attitudes toward music and potentially lead to a higherappreciation of the artistic work.

Pleasure resulting from complex music like AM could alsobe understood in terms of what Brattico called “consciousenjoyment” when conceptualizing a sensory and a “conceptualhypothesis of musical pleasure” (Brattico, 2015, p. 304). There itwas pointed out that the positive aesthetic experience obtainablefrom the Sonata No. 1 by Pierre Boulez represents a “peculiarcase of discrepancy between ‘sensory’ and ‘conscious’ enjoyment”(p. 309) and argued that although listening to an atonal piecemay lead to unpleasant sensory experience linked to activationsin the parahippocampal gyrus and the (right) amygdala (Brattico,2015), individuals may still give a positive evaluation due to aparticular structure they discovered or due to a repetitive patternthat created a familiarity effect. That an unpleasant sensoryexperience or negative emotions may be experienced differentlyin an aesthetic context compared to real life situations has beenpointed out several times. Indeed, it has been argued that negativeemotions can even reinforce the intensity of a positive aestheticexperience (Zelle, 1985; Wald-Fuhrmann, 2011; Menninghauset al., 2017).

Processes behind aesthetic framing and cognitive masteringprovide the listener with concepts and abstract information thatmodulate attitude and expectations a priori even before the musicstarts. We hypothesize that these processes play a crucial rolein the appreciation of AM because it intrinsically requires thisaesthetic attitude (Cupchik and László, 1992) being a stimulusthat would not be sought out in a non-artistic environment.Framing of AM as art in combination with an aesthetic discoursethat positively emphasizes complexity and novelty may be anecessary condition for this music to be enjoyed. In thisregard, AM represents a compelling resource for uncoveringthe neural correlates of the aesthetic attitude. A behavioral firststep to test the relevance of this aesthetic attitude could be







to compare aesthetic judgments (e.g., liking ratings) in tonaland AM after the listeners have received information (e.g.,date of composition, the piece and its historical importance,or information related to musical structure). In a between-subjects design one group would be given such informationin advance whereas the other group would not receive anyinformation before listening to the pieces. We would predict thatthe information has a stronger influence on liking ratings of AMthan those of TM.

Finally, it has been suggested that attention in a musicalexperience are necessary components of the aesthetic attitude orstance (Brattico et al., 2013; Brattico E. et al., 2017; Bundgaard,2015). Critically, AM affords an exemplary form of aestheticmusical experiences for systematic investigation since listeningto AM tends to be a fully engrossing activity and is rarely donein parallel with other activities (running, doing homework and soon). This is in sharp contrast to the way with which other morepopular forms of music (Schäfer and Sedlmeier, 2009; Greb et al.,2017) are engaged.

Intrinsic FactorsMere Exposure EffectA popular explanation for listeners’ appreciation of music isthat of repeated listening leading to an inevitable internalizationand consequent appreciation of the musical structure. Repeatedexposure has been shown to lead to an increase in liking inmany domains, a finding often referred to as the mere exposureeffect (Zajonc, 1968). Increasing familiarity with a stimulus caneffectively lead to liking even in the case of dissonant music(Omigie et al., 2017). Since AM entails a high degree of noveltyand is accordingly difficult to remember, repeated listening mayeven more than in the case of TM, play a crucial role inappreciation and preference. Specifically, listeners might learn agiven piece of music to the extent that they can form veridicalexpectations (i.e., piece-specific expectations; Snyder, 2000) atcertain points in the music, while other less learned momentsmay still induce a (pleasurable) prediction error (PE). Moreover,general familiarity with AM as a musical style – resulting fromrepeated listening of single pieces – contributes to an increasein liking since listeners begin to form schematic expectations(Snyder, 2000; Huron, 2006).

Learning mechanisms would facilitate stimulus perceptionand processing, allowing appreciation to arise from ‘processingfluency,’ the notion that enjoyment of art can result from smoothprocessing (Reber et al., 2004). One study from the computationalmodeling domain supports the notion that learning mechanismshave considerable effects on the processing of serial music:a computational model that had been trained on a corpusof artificially composed serial music pieces was able to betterpredict pieces of Arnold Schoenberg and Anton Webern (Deanand Pearce, 2016) than a corpus which was trained on tonalpieces. This suggests that an exposure to a serially composedstructure could lead to a more predictable listening experience.This predictability in turn allows a better fluency in processingwhich has been hypothesized to be linked with a pleasurableresponse. The more fluent an artwork can be perceived, themore pleasure the perceiver obtains from the object (Reber

et al., 2004). Since processing fluency is positively influenced byvariables such as symmetry, repetition, figural goodness and evenby priming procedures, it is likely a critical factor for perceptuallychallenging stimuli like AM. Similarly, Daniel Berlyne’s theoryof optimal arousal (Berlyne, 1971) states that the pleasantnessof a stimulus is influenced by properties of the stimulus that hecalled “collative or structural properties” (Berlyne, 1971, p. 81).These properties influence the stimulus’ arousal potential andaccordingly its hedonic value. The optimal arousal represents themost pleasurable state and occurs when the stimulus has reacheda medium level between simplicity and complexity, as well asbetween familiarity and novelty (Berlyne, 1970). Atonal musicmight result in a high arousal state due to its high complexityand high novelty (Berlyne, 1954) at least partially explaining whymost music listener have no preference for this kind of music.However, by virtue of repeated exposure, this complexity woulddecrease and familiarity and predictability would increase. Thiscould lead to a medium arousal level and accordingly result in anincrease in hedonic value.

In contrast to aesthetic framing and cognitive mastering,mechanisms like mere exposure result in increased processingfluency and can be said to unfold their effects during the listeningprocess. This listening process will be the focus of the followingchapters. There, the cognitive and neuronal processes involvedin processing stimuli with a high degree of uncertainty will bedescribed in greater detail.

Uncertainty as a Source of PleasureRecent approaches to musical pleasure have emphasized the roleof PE in the musical aesthetic experience. This is perhaps becausethe majority of neuroscience research on musical pleasure to datehas been carried out with Western major-minor music, whichhas a clear tonal structure that can be “played with” or can bebroken (Vuust and Kringelbach, 2010; Koelsch, 2012; Rohrmeierand Koelsch, 2012). In the following, we will first examine therelation between prediction and musical pleasure, demonstratewhich role the PE possibly plays in listening to AM and finallyshow which neural mechanism might drive the encounter as wellas enable pleasurable experience within uncertain environments.

Prediction and pleasure in music listeningIn the context of TM, researchers have been able to showthat peak moments of music-induced pleasure (indicatedby participants as chills-inducing) activate the reward andlimbic circuits of the human brain regulated by dopamineneurotransmission, and specifically the striatum (Blood andZatorre, 2001; Salimpoor et al., 2011). In more detail, thisresearch has revealed that the anticipation of musical peakexperiences has specific neural correlates in the caudate nucleus(dorsal striatum), whereas the peak experience itself is associatedwith activity in the nucleus accumbens (ventral striatum,Salimpoor et al., 2011). Salimpoor et al. (2011) speculatedthat this shift in activity from the dorsal to ventral striatum,accompanying the buildup and experience of peak musicalexperiences, reflects expectancy and consummatory processesthat are central in music perception (Meyer, 1956; Huron,2006).







While the studies above were seminal in linking, for thefirst time, dopamine-regulated activity of the nucleus accumbensto pleasurable music listening experiences, they were alsocompelling in their interpretation of the activity of the rewardsystem in the context of reward PE and anticipatory processes(Meyer, 1956; Huron, 2006; Vuust et al., 2009). This hypothesisabout dopamine release is now prevalent in accounts of music-induced emotions (Juslin and Västfjäll, 2008; Gebauer et al., 2012;Rohrmeier and Koelsch, 2012; Salimpoor et al., 2015) and derivesfrom the observation in animal models that an unexpectedreward generate a dopamine burst more than a fully expectedrewarding outcome (Schultz et al., 1997; Schultz, 1998).

A related theory, not directly linked to pleasure (Ross andHansen, 2016), claims that the brain is a Bayesian machine,anticipating and inferring upcoming events based on the statisticsof previous sensory input (Clark, 2013). This predominanttheory of brain function states that the brain continuouslypredicts what comes next, producing models (or priors) of theenvironment that are updated after errors occur. This theorycalled predictive coding is used to account for cortical responsesto unexpected or oddball events. Specifically, it has been used toexplain the mismatch negativity (MMN), an early brain responseto regularity violations that is held to index the formationand continuous updating of predictive models of the auditoryenvironment (Näätänen, 2003; Vuust and Frith, 2008; Garridoet al., 2009; Bendixen et al., 2012; Rohrmeier and Koelsch, 2012;Lieder et al., 2013).

The theory of predictive coding states that, as a “predictionmachine,” the brain’s main function is to track and to predictchanges in the environment in order to ensure optimaladaptation (Friston, 2009; Clark, 2013). A key assumption of thetheory is that the brain constantly predicts incoming sensoryinput based on generative cognitive models on higher levels.These top–down predictions continuously encounter bottom-upsensory input and in the event that the model has generated anincorrect prediction, a PE signal is used to update the model(Kopell et al., 2000; Fontolan et al., 2014).

Precision-weighting of PE in musicWhen considering music listening in predictive coding terms, itis important to note that predictive models are highly dependenton the probabilistic distributions of the incoming sensory input.Recent theories of predictive coding emphasize that the “neuralestimations of the reliability of those predictions” are just asimportant as predictions themselves (Clark, 2016, p. 9). In otherwords, the extent to which we rely on our own predictions iscritical. The estimation of the degree to which we are certainor uncertain in our predictions and the modulation of gain inneural responses relative to this certainty is referred to as theprecision-weighting of PE (Friston, 2009).

The notion of the importance of the precision-weighting ofPE is supported by findings that the brain effectively adapts tothe degree of predictability of the stimulus and that statisticalproperties of the stimulus influence the precision of prediction(Garrido et al., 2013; Sohoglu and Chait, 2016; Heilbron andChait, 2017). Specifically, Hsu et al. (2015) were able to showthis clearly in a study in which they recorded brain responses to

target notes in three conditions: the first two in the context oflow uncertainty sequences (whereby the target note was eitherpredicted or mispredicted) and the last in the context of a highuncertainty sequence (where the target note was unpredicted).The authors revealed that the amplitude of the N1 responsediffered across conditions with the largest amplitude observablefor the mispredicted note, the medium response for the predictedand the most attenuated response for unpredicted stimuli. Thefinding that PE activity is related to the uncertainty of the ongoingcontext (Hsu et al., 2015) is highly relevant for understandinghow AM could potentially be processed.

From the perspective of evolutionary biology, uncertaintyestimation is a vital necessity. It has been suggested thatuncertainty and uncertainty estimation is essential for livingorganisms (Fiorillo et al., 2003; Bach and Dolan, 2012). Itsuggested that as they often encounter situations of uncertainreward outcomes in everyday life, it is important thatorganisms are able to track reward probabilities in orderto maximize outcomes (Tobler et al., 2005). The theoryholds that uncertainty motivates agents to learn and toexplore new environments, ultimately to reach long termgoals (Bach and Dolan, 2012). With regard to what form amechanism for reward from uncertainty might take, animalresearch has shown dopaminergic activity to track highlyuncertain situations. Fiorillo et al. (2003) showed that midbraindopamine neurons show two distinct response types in thecontext of probabilistic reward receipt; one, a brief and phasicactivation tracking increasing reward probability, and the other aslower and more sustained response tracking increasing rewarduncertainty: “The sustained, uncertainty-induced increase indopamine could act to reinforce risk-taking behavior and itsconsequent reward information” (Fiorillo et al., 2003, p. 4).These risk-taking behaviors are advantageous for learningand exploration and are from an evolutionary perspectivedistinguished from exploitation, which “is the time spent usingbehaviors with known reward values” (Kringelbach and Berridge,2009, p. 205).

However, it is unlikely that listeners of AM encounter anentirely unpredictable and uncertain auditory environment asused in the “unpredicted” condition of the study of Hsu et al.(2015), where random tone sequences were used. Since musicconsists of more parameters than tonal relationships, predictionsduring music listen may rely on other more global featureslike timbre, dynamics, musical gestures or groupings based onpitch proximity or rhythm (Brattico P. et al., 2017). These low-to mid-level features may often play a key role in predictiveprocesses serving as the anchoring points of predictive modelsfor AM. In fact, these features may create a hierarchy of theirown and could represent what Bharucha and others call “eventhierarchies” (Bharucha, 1984; Deutsch, 1984) which representsa counterpart of the tonal hierarchies within a piece of music(Krumhansl and Cuddy, 2010). The extent to which predictivemodels based on event hierarchies differ from those of tonalhierarchies is a highly interesting issue that needs theoreticaland empirical investigation, as argued in a recent work (BratticoP. et al., 2017). Nevertheless, compared to music with cleartonal relationships, the degree of predictive uncertainty in the







context of these low- to mid-level features might still be veryhigh (Imberty, 1990) and the lack of tonal relationships as wellas the missing metrical regularity strongly complicates predictionprocesses. As a consequence, AM from the 20th and 21st centurymight be classified as a music that provides a listener with arather weak predictive model stemming from the absence of tonaland metrical regularity and resulting in a fairly high amount ofuncertainty.

Prediction mechanisms in atonal musicIn Western TM, tonal hierarchies afford a strong predictivemodel which can result in certain events being perceived ashighly unexpected. Moreover, points of high information content(e.g., unexpected harmonic changes (Sloboda, 1991) in musicalpieces have been shown to generate higher physiological arousalstates (Egermann et al., 2013) associated with highly pleasurablechills and shivers (Grewe et al., 2009). With regard to highlyirregular AM, naïve listeners might initially experience a highnumber of PEs, as a result of continuing to apply tonal schemasand expectations to the AM (Ockelford and Sergeant, 2012).However, potentially even shortly after introduction to AM,experienced TM listeners’ predictive neural processes may adaptto the tonal and metrical uncertainty of AM, since the brain isknown to internalize the statistics of the environment relativelyquickly (Hsu et al., 2015; Barascud et al., 2016; Pearce, 2018).This might lead to a weaker predictive model and accordinglyto attenuated predictions as neural processes successfully adaptto the irregularity. We assume that particularly in the case ofstrongly AM which additionally lacks a metrical structure, a weakpredictive model and consequent attenuated predictions mightbe reflected in neural activations level.

However, in the case of PEs that are linked to pleasurablechills, which properties must an uncertain stimulus fulfill toproduce pleasurable PEs? Presumably, once adapted to the music,listeners might expect irregularity or “the unexpected” (Huron,2006). The question arises how pleasurable PEs can occur whenno or only weak predictions are created. Paradoxically, themost unlikely event or series of events to happen in a highlyirregular environment is increased regularity. Regularity offersthe opportunity to improve the model and might therefore beevaluated as positive. Entry of a regular pattern would stick outperceptually and give the listener the opportunity to generatepredictions. This may be expected to result in a rewardingsubjective state. Previous work shows that a shift from a randomto a regular structure results in increased sustained neural activity(Barascud et al., 2016) and may reflect the improved momentarypredictive model the perceiver has.

Taken together, we hypothesize that in AM where only weakpredictive models can be maintained, the occasional occurrenceof regularities (e.g., a tone that keeps on repeating throughouta piece; a specific timbre that occurs from time to time) maybe linked to pleasurable moments, since they allow generationof stronger predictions and since the individual is given theopportunity to update their predictive model and increaseprediction certainty. To test the notion of the relevance ofcorrect predictions for pleasure in high uncertainty contexts, onecould generate strongly atonal stimuli with varying degrees of

recognizable patterns. The more recognizable the pattern, themore the piece may be liked. Moreover, when manipulatingpieces in such a way that different patterns are built on separatelow-to-mid level features (rhythmical, dynamical, pitch groupinglevel) constituting different sorts of event hierarchies (Bharucha,1984; Deutsch, 1984), one could investigate the strengthsof different predictive models belonging to different eventhierarchies. How this is shaped by long-term exposure mightbe revealed by investigating contemporary music aficionados, aswell as by musicians who are experts in the repertoire of 20th and21st century art music.

Synthesis: Appreciation of Atonal MusicIn the previous sections, we described a number of differentfactors that may contribute to a pleasurable experience of AM.Personality traits like “openness to experience” or a higherscore in “need for cognition” may drive the motivation forengagement with art in general and 20th/21st century art musicin particular. Atonal music may therefore be linked to (auditory)exploratory behavior. Having claimed that pattern detection isthe central mechanism that affords pleasurable moments withAM, working memory capacity may also be a strong predictorof deriving enjoyment from this music. Furthermore, extrinsicfactors like framing and cognitive mastering may function ascues and serve as anchors and reference points throughoutthe listening experience. They determine attitudes in advance,might trigger “perceptual curiosity” (Jepma et al., 2012) as wellas “interest” (Silvia, 2008) and may promote motivation andwillingness to engage with the music. They may also enablethe listener to create some expectations and predictions thatin turn enhances the listeners predictive model as they usePEs to refine it. Complementarily, our particular focus withregard to intrinsic factors was to demonstrate that AM doesnot generate the strong predictive models (on the basis of keyand rhythm) that TM does and that the listener encounters astate of rather high uncertainty. Since uncertainty estimationis an evolutionary necessity for explorative behavior (Tobleret al., 2005; Bach and Dolan, 2012) we argue that processingand decoding uncertainty in (atonal) music primarily representsan incentive to further engage with this stimulus. Whetherone can speak of pleasure arising from this process remainsan open question. Here, we argue with greater conviction thatpleasure from AM may arise from the rare recognition of regularstructures and underlying patterns. Such moments of increasedregularity offer the opportunity to turn a weak into a strongpredictive model.

In closing, addressing the case of AM clearly illustrates thata pleasurable experience with music can be elicited in differentways: while TM which offers a strong predictive model mayrequire wrong predictions to lead to some pleasurable states,for AM which offers the listener only a weak predictive model,correct predictions may be more likely than wrong predictionsto lead to enjoyment. Thus, we emphasize here the importanceof the strength of a predictive model in predetermining themechanisms by which pleasure may be induced during musiclistening.







FUTURE PROSPECTS

We would like to motivate research into art music fromthe 20th/21st century for several reasons. Firstly, we suggestthata comprehensive understanding of the aesthetic experiencecan arguably only be achieved after acknowledging and thusinvestigating the diversity of artistic phenomena. As art musicfrom the 20th and 21st century follows a very different aestheticparadigm to Western TM, but is rooted in the same culturalcontext, this expansion can be expected to reveal novel insightsinto the aesthetic experience of music in general. Moreover, as aresult of the specific properties of AM examined in this paper,we suggest that the pre-classification and aesthetic framing ofAM as art facilitates the appreciation of what may otherwise beconsidered an unattractive property and argue that this paradoxmakes this music a unique musical style with which to reveal keyunderlying mechanisms of musical aesthetic experience.

Second, the process of listening to AM represents a compellingapproach with which to investigate states of uncertainty,where individuals are required to not only withstand suchuncertain states but also to remain motivated to seek outcoherence and underlying structural patterns in order toimprove their predictive models. Atonal music could serveas a tool to investigate learning and pattern recognitionmechanisms and could moreover help to unravel a variety offactors that contribute to the appreciation of such uncertainenvironments.

Finally, in addressing such an extreme form of music, itbecomes clear that the pleasure derived from music may comefrom multiple facets resulting in an equally large number of formsof positive experiences. In order to investigate such differentqualities of musical pleasure, we would like to suggest theimportance of taking into account the precision of predictionsa musical piece or musical style allows. Thus, we suggest that thestrength of a predictive model may not only be used to predict

the degree and quality of pleasure induced by music over time butalso the distinct mental states that different aesthetic experiencesmay be characterized by.

AUTHOR CONTRIBUTIONS

IM conceived and developed the idea for this manuscript andprepared the first draft. DO added substantial content andcontributed with drawing up the manuscript. EB contributedto the computational feature analysis. EB and MW-F providedrevisions for important intellectual content. All authors gave finalapproval of the manuscript to be published and have agreedto be held accountable for the accuracy and integrity of thismanuscript.

ACKNOWLEDGMENTS

We would like to thank Klaus Frieler, Niels Trusbak Haumann,and Franz Schwarzacher for helping with the musical analyses.We also thank Christoph Seibert and William Barnes forinspiring discussions and for the useful comments on themanuscript. Moreover, we thank the reviewers for very helpfulcomments on an earlier version of this manuscript. Last butnot least the first author thanks all colleagues from the MPIEAand MIB for fruitful and inspiring discussions upon thistopic.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fnins.2018.00979/full#supplementary-material

REFERENCESAdorno, T. W. (1949). Philosophy of New Music. Tübingen: J.C.B. Mohr (Paul

Siebeck).Alluri, V., Brattico, E., Toiviainen, P., Burunat, I., Bogert, B., Numminen, J., et al.

(2015). Musical expertise modulates functional connectivity of limbic regionsduring continuous music listening. Psychomusicology 25, 443–454. doi: 10.1037/pmu0000124

Alluri, V., Toiviainen, P., Burunat, I., Kliuchko, M., Vuust, P., and Brattico, E.(2017). Connectivity patterns during music listening: evidence for action-basedprocessing in musicians. Hum. Brain Mapp. 2970, 2955–2970. doi: 10.1002/hbm.23565

Alluri, V., Toiviainen, P., Jääskeläinen, I. P., Glerean, E., Sams, M., and Brattico, E.(2012). Large-scale brain networks emerge from dynamic processing of musicaltimbre, key and rhythm. Neuroimage 59, 3677–3689. doi: 10.1016/j.neuroimage.2011.11.019

Bach, D. R., and Dolan, R. J. (2012). Knowing how much you don’t know: aneural organization of uncertainty estimates. Nat. Rev. Neurosci. 13, 572–586.doi: 10.1038/nrn3289

Ball, P. (2011). Schoenberg, serialism and cognition: whose fault if No one listens?Interdiscip. Sci. Rev. 36, 24–41. doi: 10.1179/030801811X12941390545645

Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., and Chait, M. (2016).Brain responses in humans reveal ideal observer-like sensitivity to complex

acoustic patterns. Proc. Natl. Acad. Sci. U.S.A. 113, E616–E625. doi: 10.1073/pnas.1508523113

Bendixen, A., SanMiguel, I., and Schröger, E. (2012). Early electrophysiologicalindicators for predictive processing in audition: a review.Int. J. Psychophysiol. 83, 120–131. doi: 10.1016/j.ijpsycho.2011.08.003

Berlyne, D. E. (1954). A theory of human curiosity. Br. J. Psychol. 45, 180–191.doi: 10.1111/j.2044-8295.1954.tb01243.x

Berlyne, D. E. (1970). Novelty, complexity, and hedonic value. Percept. Psychophys.8, 279–286. doi: 10.3758/BF03212593

Berlyne, D. E. (1971). Aesthetics and Psychobiology. New York, NY: Appleton-Century-Crofts.

Bevins, R. A. (2001). Novelty seeking and reward: implications for the study ofhigh-risk behaviors. Curr. Dir. Psychol. Sci. 10, 189–193. doi: 10.1111/1467-8721.00146

Bharucha, J. J. (1984). Event hierarchies, tonal hierarchies and assimilation: a replyto Deutsch and Dowling. J. Exp. Psychol. Gen. 113, 421–425. doi: 10.1037/0096-3445.113.3.421

Bharucha, J. J. (2014). The cortical topography of tonal structures underlyingwestern music. Science 298, 2167–2170.

Bharucha, J. J., and Krumhansl, C. L. (1983). The representation of harmonicstructure in music: hierarchies of stability as a function of context. Cognition13, 63–102. doi: 10.1016/0010-0277(83)90003-3


https://www.frontiersin.org/articles/10.3389/fnins.2018.00979/full#supplementary-material

https://www.frontiersin.org/articles/10.3389/fnins.2018.00979/full#supplementary-material

https://doi.org/10.1037/pmu0000124

https://doi.org/10.1037/pmu0000124

https://doi.org/10.1002/hbm.23565


https://doi.org/10.1016/j.neuroimage.2011.11.019


https://doi.org/10.1038/nrn3289

https://doi.org/10.1179/030801811X12941390545645

https://doi.org/10.1073/pnas.1508523113


https://doi.org/10.1016/j.ijpsycho.2011.08.003


https://doi.org/10.1111/j.2044-8295.1954.tb01243.x

https://doi.org/10.3758/BF03212593

https://doi.org/10.1111/1467-8721.00146

https://doi.org/10.1111/1467-8721.00146

https://doi.org/10.1037/0096-3445.113.3.421

https://doi.org/10.1037/0096-3445.113.3.421

https://doi.org/10.1016/0010-0277(83)90003-3






Bidelman, G. M., and Krishnan, A. (2009). Neural correlates of consonance,dissonance, and the hierarchy of musical pitch in the human brainstem.J. Neurosci. 29, 13165–13171. doi: 10.1523/JNEUROSCI.3900-09.2009

Bishop, C. (2005). Installation Art. London: Tate Publishing.Blood, A. J., and Zatorre, R. J. (2001). Intensely pleasurable responses to

music correlate with activity in brain regions implicated in reward andemotion. Proc. Natl. Acad. Sci. U.S.A. 98, 11818–11823. doi: 10.1073/pnas.191355898

Blood, A. J., Zatorre, R. J., Bermudez, P., and Evans, A. C. (1999). Emotionalresponses to pleasant and unpleasant music correlate with activity in paralimbicbrain regions. Nat. Neurosci. 2, 382–387. doi: 10.1038/7299

Bogert, B., Numminen-Kontti, T., Gold, B., Sams, M., Numminen, J., Burunat, I.,et al. (2016). Hidden sources of joy, fear, and sadness: explicit versus implicitneural processing of musical emotions. Neuropsychologia 89, 393–402. doi:10.1016/J.NEUROPSYCHOLOGIA.2016.07.005

Brattico, E. (2015). “From pleasure to liking and back: bottom-up and top-downneural routes to the aesthetic enjoyment of music,” in Art, Aesthetics and theBrain, eds J. P. Huston, M. Nadal, F. Mora, L. F. Agnati, and C. J. Cela-Conde(New York, NY: Oxford University Press), 303–318.

Brattico, E., Alluri, V., Bogert, B., Jacobsen, T., Vartiainen, N., Nieminen, S.,et al. (2011). A functional MRI study of happy and sad emotions inmusic with and without lyrics. Front. Psychol. 2:308. doi: 10.3389/fpsyg.2011.00308

Brattico, E., Bogert, B., and Jacobsen, T. (2013). Toward a neural chronometry forthe aesthetic experience of music. Front. Psychol. 4:206. doi: 10.3389/fpsyg.2013.00206

Brattico, E., Brattico, P., and Vuust, P. (2017). The forgotten artist: why to considerintentions and interaction in a model of aesthetic experience: comment on“Move me, astonish me. delight my eyes and brain: the Vienna IntegratedModel of top–down and bottom–up processes in Art Perception (VIMAP)and corresponding affective, evaluative, and neurophysiological correlates” byMatthew Pelowski et al. Phys. Life Rev. 21, 128–130. doi: 10.1016/J.PLREV.2017.06.014

Brattico, P., Brattico, E., and Vuust, P. (2017). Global sensory qualities and aestheticexperience in music. Front. Neurosci. 11:159. doi: 10.3389/fnins.2017.00159

Bregman, A. S. (1990). Auditory Scene analysis: the Perceptual Organization ofSound. Cambridge, MA: MIT Press.

Bundgaard, P. F. (2015). Feeling, meaning, and intentionality—a critique of theneuroaesthetics of beauty. Phenomenol. Cogn. Sci. 14, 781–801. doi: 10.1007/s11097-014-9351-5

Burunat, I., Toiviainen, P., Alluri, V., Bogert, B., Ristaniemi, T., Sams, M.,et al. (2016). The reliability of continuous brain responses during naturalisticlistening to music. Neuroimage 124, 224–231. doi: 10.1016/j.neuroimage.2015.09.005

Cacioppo, J. T., Petty, R. E., Feinstein, J. A., Blair, W., and Jarvis, G. (1996).Dispositional differences in cognitive motivation: the life and times ofindividuals varying in need for cognition. Psychol. Bull. 119, 197–253. doi:10.1037/0033-2909.119.2.197

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and thefuture of cognitive science. Behav. Brain Sci. 36, 181–253. doi: 10.1017/S0140525X12000477

Clark, A. (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind.Oxford: Oxford University Press. doi: 10.1093/acprof:oso/9780190217013.001.0001

Clarke, D. (2017). Defining twentieth- and twenty-first-century music. Twent.Century Music 14, 411–462. doi: 10.1017/S1478572217000342

Clarke, E. F., and Krumhansl, C. L. (2017). Perceiving musical time. Music Percept.7, 213–251. doi: 10.2307/40285462

Costa, P. T., and Mccrae, R. R. (1992). Normal personality assessment in clinicalpractice: the NEO personality inventory. Psychol. Assess. 4, 5–13. doi: 10.1037/1040-3590.4.1.5

Creel, S. C., Newport, E. L., and Aslin, R. N. (2004). Distant melodies:statistical learning of nonadjacent dependencies in tone sequences. J. Exp.Psychol. Learn. Mem. Cogn. 30, 1119–1130. doi: 10.1037/0278-7393.30.5.1119

Cross, I., Howell, P., and West, R. (1983). Preferences for scale structure in melodicsequences. J. Exp. Psychol. Hum. Percept. Perform. 9, 444–460. doi: 10.1037/0096-1523.9.3.444

Cuddy, L. L., Cohen, A. J., and Mewhort, D. J. K. (1981). Perception of structurein short melodic sequences. J. Exp. Psychol. Hum. Percept. Perform. 7, 869–883.doi: 10.1037/0096-1523.7.4.869

Cupchik, G. C., and László, J. (1992). Emerging Visions of the Aesthetic Process: InPsychology, Semiology, and Philosophy. New York, NY: Cambridge UniversityPress.

Curtis, M. E., and Bharucha, J. J. (2009). Memory and musical expectation fortones in cultural context. Music Percept. 26, 365–376. doi: 10.1525/mp.2009.26.4.365

Dahlhaus, C. (1967). Untersuchungen über die Entstehung der HarmonischenTonalität, eds H. Danuser and C. Dahlh (Kassel: Laaber Verlag).

Dahlhaus, C. (1978). “Emanzipation der dissonanz,” in Schönberg und Andere.Gesammelte Texte zur Neuen Musik, ed. C. Dahlhaus (Mainz: Schott), 146–153.

Dahlhaus, C. (1990). Studies on the Origin of Harmonic Tonality.Princeton, NJ: Princeton University Press. doi: 10.1515/9781400861316

Dean, R. T., and Bailes, F. (2016). Modeling perceptions of valence in diverse music:roles of acoustical features, agency, and individual variation. Music Percept. 34,104–117. doi: 10.1525/.2016.34.1.104

Dean, R. T., and Pearce, M. T. (2016). Algorithmically-generated corpora that useserial compositional principles can contribute to the modeling of sequentialpitch structure in non-tonal music. Empir. Musicol. Rev. 11, 27–46. doi: 10.18061/emr.v11i1.4900

Deliège, I. (1989). A perceptual approach to contemporary musical forms.Contemp. Music Rev. 4, 213–230. doi: 10.1080/07494468900640301

Deutsch, D. (1984). Two issues concerning tonal hierarchies: comment onCastellano, Bharucha, and Krumhansl. J. Exp. Psychol. Gen. 113, 413–416. doi:10.1037/0096-3445.113.3.413

Deutsch, D. (1999). The Psychology of Music. Cambridge, MA: Academic Press.Deutsch, D. (2013). “Grouping mechanisms in music,” in The Psychology of Music,

ed. D. Deutsch (San Diego, CA: Elsevier Academic Press), 299–348. doi: 10.1016/B978-0-12-381460-9.00006-7

Dewitt, L. A., and Crowder, R. G. (1987). Tonal fusion of consonant musicalintervals: the oomph in Stumpf. Percept. Psychophys. 41, 73–84. doi: 10.3758/BF03208216

Dibben, N. (1994). The cognitive reality of hierarchic structure in tonal and atonalmusic. Music Percept. 12, 1–25. doi: 10.2307/40285753

Dibelius, U. (1988). Moderne Musik. Munich: Piper.Dowling, W. J., Kwak, S., and Andrews, M. W. (1995). The time course of

recognition of novel melodies. Percept. Psychophys. 57, 136–149. doi: 10.3758/BF03206500

Ebeling, M. (2008). Neuronal periodicity detection as a basis for the perceptionof consonance: a mathematical model of tonal fusion. J. Acoust. Soc. Am. 124,2320–2329. doi: 10.1121/1.2968688

Eerola, T. (2012). Modeling listeners’ emotional response to music. Top. Cogn. Sci.4, 607–624. doi: 10.1111/j.1756-8765.2012.01188.x

Egermann, H., Pearce, M. T., Wiggins, G. A., and McAdams, S. (2013).Probabilistic models of expectation violation predict psychophysiologicalemotional responses to live concert music. Cogn. Affect. Behav. Neurosci. 13,533–553. doi: 10.3758/s13415-013-0161-y

Feist, G. J., and Brady, T. R. (2004). Openness to experience, non-conformity, andthe preference for abstract art. Empir. Stud. Arts 22, 77–89. doi: 10.2190/Y7CA-TBY6-V7LR-76GK

Fiorillo, C., Tobler, P., and Schultz, W. (2003). Discrete coding of rewardprobability and uncertainty by dopamine neurons. Science 299, 1898–1902.doi: 10.1126/science.1077349

Flores-Gutiérrez, E. O., Díaz, J. L., Barrios, F. A., Favila-Humara, R., Guevara,M. Á., del Río-Portilla, Y., et al. (2007). Metabolic and electric brain patternsduring pleasant and unpleasant emotions induced by music masterpieces. Int.J. Psychophysiol. 65, 69–84. doi: 10.1016/j.ijpsycho.2007.03.004

Fontolan, L., Morillon, B., Liegeois-Chauvel, C., and Giraud, A.-L. (2014).The contribution of frequency-specific activity to hierarchical informationprocessing in the human auditory cortex. Nat. Commun. 5:4694. doi: 10.1038/ncomms5694

Forte, A. (1977). The Structure of Atonal Music. New Haven, CT: Yale UniversityPress.

Forte, A. (1998). The Atonal Music of Anton Webern (Composers of the TwentiethCentury). New Haven, CT: Yale University Press.


https://doi.org/10.1523/JNEUROSCI.3900-09.2009



https://doi.org/10.1038/7299

https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2016.07.005

https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2016.07.005

https://doi.org/10.3389/fpsyg.2011.00308




https://doi.org/10.1016/J.PLREV.2017.06.014

https://doi.org/10.1016/J.PLREV.2017.06.014


https://doi.org/10.1007/s11097-014-9351-5

https://doi.org/10.1007/s11097-014-9351-5



https://doi.org/10.1037/0033-2909.119.2.197

https://doi.org/10.1037/0033-2909.119.2.197

https://doi.org/10.1017/S0140525X12000477

https://doi.org/10.1017/S0140525X12000477

https://doi.org/10.1093/acprof:oso/9780190217013.001.0001

https://doi.org/10.1093/acprof:oso/9780190217013.001.0001

https://doi.org/10.1017/S1478572217000342

https://doi.org/10.2307/40285462

https://doi.org/10.1037/1040-3590.4.1.5

https://doi.org/10.1037/1040-3590.4.1.5

https://doi.org/10.1037/0278-7393.30.5.1119

https://doi.org/10.1037/0278-7393.30.5.1119

https://doi.org/10.1037/0096-1523.9.3.444

https://doi.org/10.1037/0096-1523.9.3.444

https://doi.org/10.1037/0096-1523.7.4.869

https://doi.org/10.1525/mp.2009.26.4.365

https://doi.org/10.1525/mp.2009.26.4.365

https://doi.org/10.1515/9781400861316

https://doi.org/10.1515/9781400861316

https://doi.org/10.1525/.2016.34.1.104

https://doi.org/10.18061/emr.v11i1.4900

https://doi.org/10.18061/emr.v11i1.4900

https://doi.org/10.1080/07494468900640301

https://doi.org/10.1037/0096-3445.113.3.413

https://doi.org/10.1037/0096-3445.113.3.413

https://doi.org/10.1016/B978-0-12-381460-9.00006-7

https://doi.org/10.1016/B978-0-12-381460-9.00006-7

https://doi.org/10.3758/BF03208216

https://doi.org/10.3758/BF03208216

https://doi.org/10.2307/40285753

https://doi.org/10.3758/BF03206500

https://doi.org/10.3758/BF03206500

https://doi.org/10.1121/1.2968688

https://doi.org/10.1111/j.1756-8765.2012.01188.x

https://doi.org/10.3758/s13415-013-0161-y

https://doi.org/10.2190/Y7CA-TBY6-V7LR-76GK

https://doi.org/10.2190/Y7CA-TBY6-V7LR-76GK

https://doi.org/10.1126/science.1077349


https://doi.org/10.1038/ncomms5694

https://doi.org/10.1038/ncomms5694






Fricke, S. (2011). “Contemporary music,” in Musical Life in Germany - Structure,Facts and Figure, eds S. Schulmeistrat and M. Wallscheid (Bonn: German MusicCouncil), 169–183.

Friston, K. (2009). The free-energy principle: a rough guide to the brain? TrendsCogn. Sci. 13, 293–301. doi: 10.1016/j.tics.2009.04.005

Gagnon, L., and Peretz, I. (2000). Laterality effects in processing tonal and atonalmelodies with affective and nonaffective task instructions. Brain Cogn. 43,206–210. doi: 10.1006/brcg.1999.1133

Garrido, M. I., Kilner, J. M., Stephan, K. E., and Friston, K. J. (2009). Themismatch negativity: a review of underlying mechanisms. Clin. Neurophysiol.120, 453–463. doi: 10.1016/j.clinph.2008.11.029

Garrido, M. I., Sahani, M., and Dolan, R. J. (2013). Outlier responses reflectsensitivity to statistical structure in the human brain. PLoS Comput. Biol.9:e1002999. doi: 10.1371/journal.pcbi.1002999

Gebauer, L., Kringelbach, M. L., and Vuust, P. (2012). Ever-changing cycles ofmusical pleasure: the role of dopamine and anticipation. Psychomusicology 22,152–167. doi: 10.1037/a0031126

Gill, K. Z., and Purves, D. (2009). A biological rationale for musical scales. PLoSOne 4:e8144. doi: 10.1371/journal.pone.0008144

Godøy, R. I., and Leman, M. (2010). Musical Gestures. Sound, Movement, andMeaning. Abingdon: Routledge. doi: 10.4324/9780203863411

Goffman, E. (1974). Frame Analysis: An Essay on the Organization of Experience.New York, NY: Northeastern University Press.

Grant, M. J. (2001). Serial Music, Serial Aesthetics. Compositional Theory in Post-War Europe. Cambridge: Cambridge University Press.

Greb, F., Schlotz, W., and Steffens, J. (2017). Personal and situational influenceson the functions of music listening. Psychol. Music 46, 763–794. doi: 10.1177/0305735617724883

Greenwood, D. D. (1991). Critical bandwidth and consonance in relation tocochlear frequency- position coordinates. Hear. Res. 54, 164–208. doi: 10.1016/0378-5955(91)90117-R

Grewe, O., Kopiez, R., and Altenmüller, E. (2009). Chills as an indicator ofindividual emotional peaks. Ann. N. Y. Acad. Sci. 1169, 351–354. doi: 10.1111/j.1749-6632.2009.04783.x

Hansen, N. C., and Pearce, M. T. (2014). Predictive uncertainty in auditorysequence processing. Front. Psychol. 5:1052. doi: 10.3389/fpsyg.2014.01052

Hargreaves, D. J. (2012). Musical imagination: perception and production, beautyand creativity. Psychol. Music 40, 539–557. doi: 10.1177/0305735612444893

Heilbron, M., and Chait, M. (2017). Great expectations: is there evidence forpredictive coding in auditory cortex? Neuroscience 389, 54–73. doi: 10.1016/J.NEUROSCIENCE.2017.07.061

Hsu, Y., Le Bars, S., and Ha, J. A. (2015). Distinctive representation of mispredictedand unpredicted prediction errors in human electroencephalography.J. Neurosci. 35, 14653–14660. doi: 10.1523/JNEUROSCI.2204-15.2015

Hüppe, E. (2016). “Popularität,” in Lexikon Neue Musik, eds P. Hiekel and C. Utz(Kassel: Metzler), 510–514.

Huron, D. (2001). Tone and voice: a derivation of the rules of voice-leading fromperceptual principles. Music Percept. 19, 1–64. doi: 10.1525/mp.2001.19.1.1

Huron, D. (2006). Sweet Anticipation: Music and the Psychology of Expectation.Cambridge, MA: MIT Press.

Hyer, B. (2001). “Tonality,” in Grove Music Online, ed. L. Macy (Oxford: OxfordUniversity Press). doi: 10.1093/gmo/9781561592630.article.28102

Imberty, M. (1990). “How do we perceive atonal music? Suggestions for atheoretical approach,” in Proceedings of Cambridge Conference on Music and theCognitive Sciences, eds I. Cross and I. Deliege (Cambridge: MIT Press), 336–366.

Jacobsen, T., and Beudt, S. (2017). Stability and variability in aesthetic experience:a review. Front. Psychol. 8:143. doi: 10.3389/fpsyg.2017.00143

Janata, P. (2013). The cortical topography of tonal structures underlying westernmusic. Science 298, 2167–2170. doi: 10.1126/science.1076262

Jepma, M., Verdonschot, R. G., van Steenbergen, H., Rombouts, S. A., andNieuwenhuis, S. (2012). Neural mechanisms underlying the induction and reliefof perceptual curiosity. Front. Behav. Neurosci. 6:5. doi: 10.3389/fnbeh.2012.00005

Juslin, P. N. (2013). From everyday emotions to aesthetic emotions: towards aunified theory of musical emotions. Phys. Life Rev. 10, 235–266. doi: 10.1016/j.plrev.2013.05.008

Juslin, P. N., and Sloboda, J. A. (2010). Handbook of Music and Emotion: Theory,Research, Applications. Oxford: Oxford University Press.

Juslin, P. N., and Västfjäll, D. (2008). Emotional responses to music: the need toconsider underlying mechanisms. Behav. Brain Sci. 31, 559–621. doi: 10.1017/S0140525X08005293

Koelsch, S. (2012). Brain and Music. Hoboken, NJ: Wiley-Blackwell.Koelsch, S., Fritz, T., Cramon, D. Y. V., Müller, K., and Friederici, A. D. (2006).

Investigating emotion with music: an fMRI study. Hum. Brain Mapp. 27,239–250. doi: 10.1002/hbm.20180

Konecni, V. (2005). The aesthetic trinity: awe, being moved, thrills. Bull. Psychol.Arts 5, 27–44.

Kopell, N., Ermentrout, G. B., Whittington, M. A., and Traub, R. D. (2000). Gammarhythms and beta rhythms have different synchronization properties. Proc. Natl.Acad. Sci. U.S.A. 97, 1867–1872. doi: 10.1073/pnas.97.4.1867

Kringelbach, M. L., and Berridge, K. C. (2009). Pleasures of the Brain. Oxford:Oxford University Press.

Krumhansl, C. L. (1979). The psychological representation of musical pitchin a tonal context. Cogn. Psychol. 11, 346–374. doi: 10.1016/0010-0285(79)90016-1

Krumhansl, C. L. (1990). Cognitive Foundations of Musical Pitch. Oxford: OxfordUniversity Press.

Krumhansl, C. L., and Cuddy, L. (2010). “A theory of tonal hierarchies in music,”in Music Perception, Springer Handbook of Auditory Research, Vol. 36, edsM. R. Jones, R. R. Fay, and A. N. Popper (New York, NY: Springer), 51–87.doi: 10.1007/978-1-4419-6114-3_3

Krumhansl, C. L., Sandell, G. J., and Sergeant, D. C. (1987). The perception oftone hierarchies and mirror forms in twelve-tone serial music. Music Percept.5, 31–78. doi: 10.2307/40285385

Krumhansl, C. L., Toivanen, P., Eerola, T., Toiviainen, P., Järvinen, T., andLouhivuori, J. (2000). Cross-cultural music cognition: cognitive methodologyapplied to North Sami yoiks. Cognition 76, 13–58. doi: 10.1016/S0010-0277(00)00068-8

Lartillot, O., Eerola, T., Toiviainen, P., and Fornari, J. (2008). “Multi-featuremodeling of pulse clarity: design, validation and optimisation,” in Proceedings ofthe International Conference on Music Information Retrieval, Philadelphia, PA.

Lartillot, O., and Toiviainen, P. (2007a). “A matlab toolbox for musical featureextraction from audio,” in Proceedings of the International Conference on DigitalAudio, Aveiro.

Lartillot, O., and Toiviainen, P. (2007b). “MIR in matlab (II): a toolbox for musicalfeature extraction from audio,” in Proceedings of 8th International Conferenceon Music Information Retrieval, Vienna, 140.

Leder, H., Belke, B., Oeberst, A., and Augustin, D. (2004). A model of aestheticappreciation and aesthetic judgments. Br. J. Psychol. 95, 489–508. doi: 10.1348/0007126042369811

Lerdahl, F., and Jackendoff, R. (1983). A Generative Theory of Tonal Music.Cambridge, MA: MIT Press.

Levine, D. S. (2012). “I think therefore I feel: possible neural mechanisms forknowledge-based pleasure,” in Proceedings of the International Joint Conferenceon Neural Networks, Brisbane. doi: 10.1109/IJCNN.2012.6252413

Levitin, D. J., and Tirovolas, A. K. (2009). Current advances in the cognitiveneuroscience of music. Ann. N. Y. Acad. Sci. 1156, 211–231. doi: 10.1111/j.1749-6632.2009.04417.x

Lieder, F., Stephan, K. E., Daunizeau, J., Garrido, M. I., and Friston, K. J. (2013).A neurocomputational model of the mismatch negativity. PLoS Comput. Biol.9:e1003288. doi: 10.1371/journal.pcbi.1003288

Maletz, H. (2011). Leidenschaft? Neue Musik. Über Klänge, Laute, Zeichen bis zuJazz und Pop. Berlin: Lit Verlag.

Mandler, J. M. (1984). Stories, Scripts, and Scenes: Aspects of Schema Theory.Hillsdale, NJ: Erlbaum.

Meißner, J., and Morawek, M. (2012). Leidenschaft Neue Musik. Ein FilmÜber Vielfalt und Freiheiten im Ensemble Modern. Deutschland: HessischerRundfunk; Reihe: Wissen und Mehr.

Menninghaus, W., Wagner, V., Hanich, J., Wassiliwizky, E., Jacobsen, T., andKoelsch, S. (2017). The DISTANCING–EMBRACING model of the enjoymentof negative emotions in art reception. Behav. Brain Sci. 40:e347. doi: 10.1017/S0140525X17000309

Meyer, L. B. (1956). Emotion and Meaning in Music. Chicago, IL: University ofChicago Press.

Mikumo, M. (1992). Encoding strategies for tonal and atonal melodies. MusicPercept. 10, 73–82. doi: 10.2307/40285539


https://doi.org/10.1016/j.tics.2009.04.005

https://doi.org/10.1006/brcg.1999.1133

https://doi.org/10.1016/j.clinph.2008.11.029

https://doi.org/10.1371/journal.pcbi.1002999

https://doi.org/10.1037/a0031126

https://doi.org/10.1371/journal.pone.0008144

https://doi.org/10.4324/9780203863411

https://doi.org/10.1177/0305735617724883

https://doi.org/10.1177/0305735617724883

https://doi.org/10.1016/0378-5955(91)90117-R

https://doi.org/10.1016/0378-5955(91)90117-R

https://doi.org/10.1111/j.1749-6632.2009.04783.x

https://doi.org/10.1111/j.1749-6632.2009.04783.x


https://doi.org/10.1177/0305735612444893

https://doi.org/10.1016/J.NEUROSCIENCE.2017.07.061

https://doi.org/10.1016/J.NEUROSCIENCE.2017.07.061


https://doi.org/10.1525/mp.2001.19.1.1

https://doi.org/10.1093/gmo/9781561592630.article.28102



https://doi.org/10.3389/fnbeh.2012.00005

https://doi.org/10.3389/fnbeh.2012.00005

https://doi.org/10.1016/j.plrev.2013.05.008


https://doi.org/10.1017/S0140525X08005293

https://doi.org/10.1017/S0140525X08005293


https://doi.org/10.1073/pnas.97.4.1867

https://doi.org/10.1016/0010-0285(79)90016-1

https://doi.org/10.1016/0010-0285(79)90016-1

https://doi.org/10.1007/978-1-4419-6114-3_3

https://doi.org/10.2307/40285385

https://doi.org/10.1016/S0010-0277(00)00068-8

https://doi.org/10.1016/S0010-0277(00)00068-8

https://doi.org/10.1348/0007126042369811

https://doi.org/10.1348/0007126042369811

https://doi.org/10.1109/IJCNN.2012.6252413

https://doi.org/10.1111/j.1749-6632.2009.04417.x

https://doi.org/10.1111/j.1749-6632.2009.04417.x

https://doi.org/10.1371/journal.pcbi.1003288

https://doi.org/10.1017/S0140525X17000309

https://doi.org/10.1017/S0140525X17000309

https://doi.org/10.2307/40285539






Mussel, P. (2014). Need for Cognition. In Dorsch – Lexikon der Psychologie. Wirtz,VA: Markus Antonius, 1084.

Näätänen, R. (2003). Mismatch negativity: clinical research and possibleapplications. Int. J. Psychophysiol. 48, 179–188. doi: 10.1016/S0167-8760(03)00053-9

Narmour, E. (1990). The Analysis and Cognition of Basic Melodic Structures: TheImplication-Realization Model. Chicago, IL: University of Chicago Press.

Nelson, M., El Karoui, I., Giber, K., Yang, X., Cohen, L., Koopman, H., et al. (2017).Neurophysiological dynamics of phrase-structure building during sentenceprocessing. Proc. Natl. Acad. Sci. U.S.A. 114:201701590. doi: 10.1073/pnas.1701590114

Nusbaum, E. C., and Silvia, P. J. (2011). Shivers and timbres: personality andthe experience of chills from music. Soc. Psychol. Pers. Sci. 2, 199–204. doi:10.1177/1948550610386810

Ockelford, A., and Sergeant, D. (2012). Musical expectancy in atonal contexts:musicians’ perception of “antistructure.” Psychol. Music 41, 139–174. doi: 10.1177/0305735612442582

Omigie, D., Dellacherie, D., and Samson, S. (2017). Effects of learning ondissonance judgments. J. Interdiscip. Music Stud. 8, 11–28. doi: 10.4407/jims.2016.12.001

Patel, A. D. (2003). Language, music, syntax and the brain. Nat. Neurosci. 6,674–681. doi: 10.1038/nn1082

Pearce, M. T. (2005). The Construction and Evaluation of Statistical Models ofMelodic Structure in Music Perception and Composition. Doctoral Dissertation,City University, London.

Pearce, M. T. (2018). Statistical learning and probabilistic prediction in musiccognition: mechanisms of stylistic enculturation. Ann. N. Y. Acad. Sci. 1423,378–395. doi: 10.1111/nyas.13654

Peciña, M., Mickey, B. J., Love, T., Wang, H., Langenecker, S. A., Hodgkinson, C.,et al. (2013). DRD2 polymorphisms modulate reward and emotion processing,dopamine neurotransmission and openness to experience. Cortex 49, 877–890.doi: 10.1016/j.cortex.2012.01.010

Pelowski, M., Markey, P. S., Forster, M., Gerger, G., and Leder, H. (2017). Move me,astonish me. . . delight my eyes and brain: the Vienna Integrated Model of top-down and bottom-up processes in Art Perception (VIMAP) and correspondingaffective, evaluative, and neurophysiological correlates. Phys. Life Rev. 21,80–125. doi: 10.1016/j.plrev.2017.02.003

Perlovsky, L. I., and Levine, D. S. (2012). The drive for creativity and the escapefrom creativity: neurocognitive mechanisms. Cognit. Comput. 4, 292–305. doi:10.1007/s12559-012-9154-3

Plomp, R., and Levelt, W. J. M. (1965). Tonal consonance and critical bandwidth.J. Acoust. Soc. Am. 38, 548–560. doi: 10.1121/1.1909741

Poikonen, H., Alluri, V., Brattico, E., Lartillot, O., Tervaniemi, M., andHuotilainen, M. (2016). Event-related brain responses while listening to entirepieces of music. Neuroscience 312, 58–73. doi: 10.1016/j.neuroscience.2015.10.061

Reber, R., Schwarz, N., and Winkielman, P. (2004). Processing fluency and aestheticpleasure: is beauty in the perceiver’s processing experience? Pers. Soc. Psychol.Rev. 8, 364–382. doi: 10.1207/s15327957pspr0804_3

Rentfrow, P. J., and Gosling, S. D. (2003). The do re Mi’s of everyday life: thestructure and personality correlates of music preferences. J. Pers. Soc. Psychol.84, 1236–1256. doi: 10.1037/0022-3514.84.6.1236

Reybrouck, M. (1997). “Gestalt concepts and music: limitations and possibilities,”in Music, Gestalt, and Computing: Studies in Cognitive and SystematicMusicology, ed. M. Leman (Berlin: Springer Verlag), 57–69.

Reybrouck, M., and Brattico, E. (2015). Neuroplasticity beyond sounds: neuraladaptations following long-term musical aesthetic experiences. Brain Sci. 5,69–91. doi: 10.3390/brainsci5010069

Rohrmeier, M. A., and Koelsch, S. (2012). Predictive information processing inmusic cognition. A critical review. Int. J. Psychophysiol. 83, 164–175. doi: 10.1016/j.ijpsycho.2011.12.010

Rosch, E. (1975). Cognitive reference points. Cogn. Psychol. 7, 532–547. doi: 10.1016/0010-0285(75)90021-3

Ross, S., and Hansen, N. C. (2016). Dissociating prediction failure: considerationsfrom music perception. J. Neurosci. 36, 3103–3105. doi: 10.1523/JNEUROSCI.0053-16.2016

Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., and Zatorre, R. J. (2011).Anatomically distinct dopamine release during anticipation and experience

of peak emotion to music. Nat. Neurosci. 14, 257–262. doi: 10.1038/nn.2726

Salimpoor, V. N., Zald, D. H., Zatorre, R. J., Dagher, A., and McIntosh, A. R. (2015).Predictions and the brain: how musical sounds become rewarding. TrendsCogn. Sci. 19, 86–91. doi: 10.1016/j.tics.2014.12.001

Schäfer, T., and Sedlmeier, P. (2009). From the functions of music tomusic preference. Psychol. Music 37, 279–300. doi: 10.1177/0305735608097247

Schultz, W. (1998). Predictive reward signal of dopamine neurons. J. Neurophysiol.80, 1–27.

Schultz, W., Dayan, P., and Montague, P. R. (1997). A neural substrate ofprediction and reward. Science 275, 1593–1599. doi: 10.1126/science.275.5306.1593

Schulze, K., Dowling, W. J., and Tillmann, B. (2012). Working memory for tonaland atonal sequences during a forward and a backward recognition task. MusicPercept. 61, 137–170.

Silvia, P. J. (2008). Interest - The curious emotion. Curr. Dir. Psychol. Sci. 17, 57–60.doi: 10.1111/j.1467-8721.2008.00548.x

Silvia, P. J., and Nusbaum, E. C. (2011). On personality and piloerection: individualdifferences in aesthetic chills and other unusual aesthetic experiences. Psychol.Aesthet. Creat. Arts 5, 208–214. doi: 10.1037/a0021914

Sloboda, J. A. (1991). Music structure and emotional response: some empiricalfindings. Psychol. Music 19, 110–120. doi: 10.1177/0305735691192002

Smith, J. D., and Melara, R. J. (1990). Aesthetic preference and syntacticprototypicality in music: ’tis the gift to be simple. Cognition 34, 279–298. doi:10.1016/0010-0277(90)90007-7

Snyder, B. (2000). Music and Memory: an Introduction. Cambridge, MA: MITPress.

Sohoglu, E., and Chait, M. (2016). Detecting and representing predictable structureduring auditory scene analysis. eLife 5:e19113. doi: 10.7554/eLife.19113

Steinbeis, N., and Koelsch, S. (2009). Understanding the intentions behindman-made products elicits neural activity in areas dedicated to mentalstate attribution. Cereb. Cortex 19, 619–623. doi: 10.1093/cercor/bhn110

Stevens, C. J. (2012). Music perception and cognition: a review of recent cross-cultural research. Top. Cogn. Sci. 4, 653–667. doi: 10.1111/j.1756-8765.2012.01215.x

Taruskin, R. (2005). Oxford History on Western Music. Oxford: Oxford UniversityPress.

Tillmann, B., Janata, P., Birk, J., and Bharucha, J. J. (2008). Tonal centers andexpectancy: facilitation or inhibition of chords at the top of the harmonichierarchy? J. Exp. Psychol. Hum. Percept. Perform. 34, 1031–1043. doi: 10.1037/0096-1523.34.4.1031

Tobler, P. N., Fiorillo, C. D., and Schultz, W. (2005). Adaptive coding of rewardvalue by dopamine neurons. Science 307, 1642–1645. doi: 10.1126/science.1105370

Toiviainen, P., and Krumhansl, C. L. (2003). Measuring and modeling real-timeresponses to music: the dynamics of tonality induction. Perception 32, 741–766.doi: 10.1068/p3312

Trainor, L. J., Tsang, C. D., and Cheung, V. H. W. (2002). Preference for sensoryconsonance in 2- and 4-month-old infants. Music Percept. 20, 187–194. doi:10.1525/mp.2002.20.2.187

Tramo, M. J., Cariani, P. A., Delgutte, B., and Braida, L. D. (2006). Neurobiologicalfoundations for the theory of harmony in western tonal music. Ann. N. Y. Acad.Sci. 930, 92–116. doi: 10.1111/j.1749-6632.2001.tb05727.x

Trost, W., Ethofer, T., Zentner, M., and Vuilleumier, P. (2012). Mapping aestheticmusical emotions in the brain. Cereb. Cortex 22, 2769–2783. doi: 10.1093/cercor/bhr353

Utz, C. (ed.). (2016). “Wahrnehmung,” in Lexikon Neue Musik, (Munich: Metzler),600–609.

van de Cruys, S., and Wagemans, J. (2011). Gestalts as predictions - somereflections and an application to art. Gestalt Theory 33, 325–344.

Vuust, P., and Frith, C. (2008). Anticipation is the key to understanding music andthe effects of music on emotion. Behav. Brain Sci. 31, 599–600. doi: 10.1017/S0140525X08005542

Vuust, P., and Kringelbach, M. L. (2010). The pleasure of making sense ofmusic. Interdiscip. Sci. Rev. 35, 166–182. doi: 10.1179/030801810X12723585301192


https://doi.org/10.1016/S0167-8760(03)00053-9

https://doi.org/10.1016/S0167-8760(03)00053-9



https://doi.org/10.1177/1948550610386810

https://doi.org/10.1177/1948550610386810

https://doi.org/10.1177/0305735612442582

https://doi.org/10.1177/0305735612442582

https://doi.org/10.4407/jims.2016.12.001

https://doi.org/10.4407/jims.2016.12.001

https://doi.org/10.1038/nn1082

https://doi.org/10.1111/nyas.13654

https://doi.org/10.1016/j.cortex.2012.01.010


https://doi.org/10.1007/s12559-012-9154-3

https://doi.org/10.1007/s12559-012-9154-3

https://doi.org/10.1121/1.1909741

https://doi.org/10.1016/j.neuroscience.2015.10.061

https://doi.org/10.1016/j.neuroscience.2015.10.061

https://doi.org/10.1207/s15327957pspr0804_3

https://doi.org/10.1037/0022-3514.84.6.1236

https://doi.org/10.3390/brainsci5010069



https://doi.org/10.1016/0010-0285(75)90021-3

https://doi.org/10.1016/0010-0285(75)90021-3



https://doi.org/10.1038/nn.2726

https://doi.org/10.1038/nn.2726

https://doi.org/10.1016/j.tics.2014.12.001

https://doi.org/10.1177/0305735608097247

https://doi.org/10.1177/0305735608097247

https://doi.org/10.1126/science.275.5306.1593

https://doi.org/10.1126/science.275.5306.1593

https://doi.org/10.1111/j.1467-8721.2008.00548.x

https://doi.org/10.1037/a0021914

https://doi.org/10.1177/0305735691192002

https://doi.org/10.1016/0010-0277(90)90007-7

https://doi.org/10.1016/0010-0277(90)90007-7

https://doi.org/10.7554/eLife.19113

https://doi.org/10.1093/cercor/bhn110

https://doi.org/10.1093/cercor/bhn110

https://doi.org/10.1111/j.1756-8765.2012.01215.x

https://doi.org/10.1111/j.1756-8765.2012.01215.x

https://doi.org/10.1037/0096-1523.34.4.1031

https://doi.org/10.1037/0096-1523.34.4.1031



https://doi.org/10.1068/p3312

https://doi.org/10.1525/mp.2002.20.2.187

https://doi.org/10.1525/mp.2002.20.2.187

https://doi.org/10.1111/j.1749-6632.2001.tb05727.x

https://doi.org/10.1093/cercor/bhr353

https://doi.org/10.1093/cercor/bhr353

https://doi.org/10.1017/S0140525X08005542

https://doi.org/10.1017/S0140525X08005542

https://doi.org/10.1179/030801810X12723585301192

https://doi.org/10.1179/030801810X12723585301192






Vuust, P., Ostergaard, L., Johanne, K., Bailey, C., and Roepstorff, A.(2009). Predictive coding of music – Brain responses torhythmic incongruity. Cortex 45, 80–92. doi: 10.1016/j.cortex.2008.05.014

Vuvan, D. T., Podolak, O. M., and Schmuckler, M. A. (2014). Memory for musicaltones: the impact of tonality and the creation of false memories. Front. Psychol.5:582. doi: 10.3389/fpsyg.2014.00582

Wald-Fuhrmann, M. (2011). “Ein Mittel wider sich selbst”: Melancholie in derInstrumentalmusik um 1800. Kassel: Bärenreiter.

Zajonc, R. B. (1968). Attitudinal effects of mere exposure. J. Pers.Soc. Psychol. Monogr. Suppl. 9(2 Pt 2), 1–27. doi: 10.1037/h0025848

Zatorre, R. J., and Salimpoor, V. N. (2013). From perception to pleasure: musicand its neural substrates. Proc. Natl. Acad. Sci. U.S.A. 110(Suppl.), 10430–10437.doi: 10.1073/pnas.1301228110

Zelle, C. (1985). „Angenehmes Grauen“. Literarhistorische Beiträge zur Ästhetikdes Schrecklichen im Achtzehnten Jahrhundert. Hamburg: Felix MeinerVerlag.

Zentner, M. R., and Kagan, J. (1998). Infants’ perception of consonance anddissonance in music. Infant Behav. Dev. 21, 483–492. doi: 10.1016/S0163-6383(98)90021-2

Zivic, P. H. R., Shifres, F., and Cecchi, G. A. (2013). Perceptual basis of evolvingWestern musical styles. Proc. Natl. Acad. Sci. U.S.A. 110, 10034–10038. doi:10.1073/pnas.1222336110

Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.

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https://doi.org/10.1037/h0025848

https://doi.org/10.1037/h0025848


https://doi.org/10.1016/S0163-6383(98)90021-2

https://doi.org/10.1016/S0163-6383(98)90021-2











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